esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Empirical Analysis
### Verification Indicator  
To interpret the effect of the DE-MPFSC algorithm on the imbalanced data integrated and classified, we need to verify the consistency of the imbalanced data. First, we should establish the verification indicator of classification accuracy (CA) [55; 56], adjusted rand index (ARI) [55; 56], normalized mutual information (NMI) [55; 56] to test the effect of the DE-MPFSC algorithm, which are described as follows.  
(1) CA: CA is a statistical mathematics variable measuring the imbalanced data samples proportion of the DE-MPFSC algorithm in making up for data integrated defects, which is expressed as follows.  
\[CA=\frac{\sum_{i=1}^{k}\alpha_{i}}{n}, \tag{34}\]  
where \(\alpha_{i}\) is a subsample of the DE-MPFSC algorithm in making up for data integrated defects, \(k\) is the number of integrated classes, \(n\) is the sample capacity.  
(2) ARI: ARI is a statistical mathematics variable measuring the imbalanced data samples proportion of the DE-MPFSC algorithm in making up for data integrated defects after considering the same data class and different data classes, which is expressed as follows.  
\[ARI=\frac{\sum_{i=1}^{I}\sum_{j=1}^{J}C_{n_{ij}}^{2}-\eta}{\frac{1}{2}(\rho+ \phi)-\eta} \tag{35}\]  
\[\rho=\sum_{i=1}^{I}C_{n_{i}}^{2},\phi=\sum_{j=1}^{J}C_{n_{j}}^{2},\eta=\frac{2 \rho\phi}{n(n-1)}. \tag{36}\]  
(3) NMI: NMI is a similarity variable measuring sample data and integrated data after comparing and correctly integrating and classing imbalanced data within being integrated defects, which is expressed as follows.  
\[NMI=\frac{\sum_{i=1}^{I}\sum_{j=1}^{J}\frac{n_{ij}^{2}n}{n,n_{ij}}}{\sqrt{\sum _{i=1}^{I}\frac{n_{i}^{2}}{n}\sum_{j=1}^{J}\frac{n_{j}^{2}}{n}}}, \tag{37}\]  
where \(n_{ij}\) is the sample size in the data integrated result where the \(i\)th datasets contains the original datasets \(j\). \(n_{i}\) is the sample size of the \(i\)th datasets in the data integrated result. \(n_{j}\) is the sample size of the original datasets \(j\). \(n\) is the samples. \(I,J\) is the number of different types of datasets and the number of original datasets obtained in the data integrated result. The limit value of the three types of verification indicators is set to 1. If the data structure is close to the original data structure after imbalanced integrated data, which means that the value of the dataset is larger compared to the original datasets. That is, the DE-MPFSC algorithm has a better effect on imbalanced data integration.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 1 Introduction  
Hong Kong is a metropolitan city of approximately 1100 km\({}^{2}\) in size, supporting a population of over seven million. Its land comprises hilly terrain with some 63% of the land steeper than 15\({}^{\circ}\) and over 30% steeper than 30\({}^{\circ}\). As a result, there is a high concentration of population and infrastructural developments near natural hills and man-made slopes, which are susceptible to landslides under heavy rainstorms. This urban setting thus calls for the continual development of the landslide risk management system of the city, which has been managed by the Geotechnical Engineering Office (GEO) since 1977 [1].  
Over the past 40 years, the GEO has been progressively establishing, maintaining, and enhancing inventories of territory-wide landslide-related data of high resolutions in both spatial and temporal terms. Examples include a comprehensive inventory of historical landslides, a LiDAR-based digital terrain model for the territory, and real-time 1-min interval rainfall records through a dense network of rain gauges over the territory (about one rain gauge per 10 km\({}^{2}\)). These high-quality data have been used to support the development of practical landslide prediction models, which are instrumental to effective landslide risk management. The landslide prediction models have been used for landslide susceptibility mapping [2] and landslide early warning [3] in Hong Kong. Historically, these models were developed mainly based on data-driven analyses using a conventional statistical approach.  
Recently, taking advantage of the versatility of machine learning (ML) techniques and the wealth of landslide-related data collated over the years, the GEO has been exploring the application of ML algorithms to landslide predictions. As an exploratory study, an ML-powered natural terrain landslide susceptibility analysis has been initiated. Details of the study, which is an expansion on the study discussed in [PERSON] et al. (2022) [4], are presented in this paper.
## 5 Conclusions and Future Work  
### Conclusions  
In this paper, a target re-identification method called TRTrack based on shot boundary object detection is proposed to solve the issue of poor single object tracking due to target out-of-view. First, we build a bipartite matching model of candidate tracklets and neighbor tracklets optimized by the Hopcroft-Karp algorithm, which is used for preliminary tracking and judging the target leaves the shot. If the target is in the shot, the tracking will continue. Otherwise, the boundary object detection module based on improved YOLOv5-DeepSORT with the alpha-IoU loss function will detect the shot boundary objects in the video. Finally, the backtracking identification module in TRTrack will crop the detected objects out, replace the current frame image with them as the search region image and input them back into the object tracking model. According to the confidence score of tracking results, the target can be re-identified in a very short time.  
The experimental results show that the preliminary object tracking model in our method achieves 73.1% AUC on LaSOT and 76.3% AO on GOT-10k. The YOLOv5-DeepSORT of the boundary object detection module obtains 38.62% mAP\({}_{75.95}\) on MS COCO 2017. The proposed target re-identification method TRTrack demonstrates its target re-identification method.  
Figure 9: Target re-identification results of some videos in the GOT-10k dataset. In these videos, there are also cases of targets out-of-view, and our model can re-identify the target in a short time.  
identification ability at the self-built dataset CLV, which is superior to most state-of-the-art models and has practical application value.
# Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management
## 4 Case Study
### RRCM Application Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Method** & **Item Study** & **Risk Estimation and Evaluation** & **Maintenance Planning** & **Periodic Review** \\ \hline [PERSON] et al. (2009)[41] & REM & The paper provides a study of the items, including the identification of fault events and consequences. & The paper presents risk estimation and evaluation for each failure & The paper does not present a support tool to select an appropriate tool to select an appropriate tool to select an appropriate tool for re-estimating or re-classifying risk. \\ \hline [PERSON], [PERSON], and [PERSON] (2008)[42] & REM & The paper provides a study of the items, including the identification of fault events and consequences. & The paper provides a study of the items, including the identification of fault events and consequences. & The paper presents risk estimation and evaluation for each failure & The paper does not present a support tool to select an appropriate tool that re-evaluates risk after developing a maintenance plan. \\ \hline [PERSON] and [PERSON] (2004)[63] & REM & The paper provides a study of the items, including the identification of fault events and consequences. & The paper provides risk estimation and evaluation for each failure & The paper does not present a support tool to select an appropriate tool that re-evaluates risk after taking management policy for each & The paper presents an iterative method that re-evaluates risk after developing a maintenance plan. \\ \hline Proposed method & REM & The proposed RRCM includes a broad study of the items, including their functions, functional failures, failure modes, failure effects, and FCC. & The proposed RRCM includes the official for the classification of the risk associated with each significant change in the selection of an appropriate failure management policy & The proposed RRCM includes the selection of an appropriate maintenance plan. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cant._Finally, although the RBM method provides in-depth risk estimation and evaluation of items and their failure scenarios, it lacks a comprehensive examination of the functions, failure modes, and functional failure effects of these items. As a consequence, the RBM does not provide sufficient guidance for maintenance planning decision-making and usually is not supported by guidelines or decision diagrams for the selection of failure management policies [40; 41; 42; 43]. Such limitation is addressed through the implementation of the proposed RRCM method that not only specifies a broad item study as in the RCM method [13; 25; 37] but also enhances it with more detailed diagrams and guidance that ensures reliability and incorporates risk and cost considerations in maintenance planning.
## 1 Introduction  
Capsule endoscopy (CE) [1,2] is a non-invasive technique to receive images of the intestine for medical diagnostics. The main design challenges of endoscopy capsule are acquiring and transmitting acceptable quality images by utilizing as little hardware and battery power as possible. In order to save wireless transmission power and bandwidth, an image compressor needs to be implemented inside an endoscopy capsule. Lossy image compressors produce some difference between the original and reconstructed images. For medical diagnostics, the distortion of the reconstructed image can lead to inaccurate diagnostics decisions, though in medical and endoscopic imaging, lossy compression is acceptable up to a certain point (for example, a compression ratio of 15 was found as the visually lossless threshold for the JPEG lossy algorithm [3]). However, in hospitals in these days where Picture Archiving and Communication Systems (PACS) are used to store medical and diagnostic data in digital form, lossless compression is a requirement [4]. In addition, lossless compressors produce identical reconstructed images compared with the original images without any distortion. Therefore, the prime objective has always been to find efficient and lossless compression methods.  
In this paper, a low complexity yet lossless image compression algorithm is presented that is purposely designed for capsule endoscopy. The compressor works on a highly efficient YEF color space [5], which is specially designed to compress endoscopic images by analyzing the unique image properties. After color space conversion, the compressor takes the difference of consecutive pixels using differential pulse coded modulation (DPCM) and then encodes the differences in variable length Golomb-Rice [6] and unary coding. A customized corner clipping scheme is also implemented to remove uninteresting corner area of the image to increase compression ratio. All these components are fully optimized for low-cost operation and lossless in nature; as a result, no loss of any diagnostic information takes place inside the endoscopic system.  
In order to validate the performance of the compression algorithm, it is deployed inside an endoscopic capsule prototype developed in our lab. At first, a modular and programmable CE development system platform consisting of a miniature field programmable gate array (FPGA) based electronic capsule is developed. The prototype supports various imaging modes including the commonly used white light imaging (WLI) and narrow band imaging (NBI) [7], and communicates with the data logger in full duplex fashion, which enables configuring the image size and imaging mode in real time during the examination. The CE prototype is then tested to assess the performance using live pig in both _ex-vivo_ and _in-vivo_ trials at the animal facility.
###### Abstract  
Background: Persistent bacterial infections caused by biofilm-associated _Enterococcus faecalis_ are a global public health concern. This study aims to compare the biofilm removal effects of methylene blue (MB) or hydrogen peroxide (HP) mediated photodynamic inactivation (PDI) with a shock wave enhanced emission photoacoustic streaming (SWEEPS) method laser irradiation enhanced bactericidal activity. Methods: A total of fifty extracted single-rooted human teeth were used. Each canal was then inoculated with a bacterial suspension of _E. faecalis_ and left for ten days to induce biofilm formation. The antibacterial effects within treated root canals using MB or HP + diode laser or SWEEPS alone or in combination were assessed and compared with 5.25% sodium hypochlorite (NaOCI) as the positive control group. Data were analyzed using one-way ANOVA and [PERSON]'s post hoc test. Results: The MB + diode laser + SWEEPS and HP + diode laser + SWEEPS groups displayed significantly lower amounts of bacteria than either the MB + diode laser or SWEEPS and HP + diode laser or SWEEPS groups (_p_ < 0.001). There was no statistically significant difference detected between the diode laser and SWEEPS groups (_p_ = 0.982). Conclusions: SWEEPS can enhance the photosensitizer distribution in the root canal, leading to effective biofilm removal. This technique could thus help promote the future development of endodontic treatments.  
Enterococcus faecalis; biofilms; disinfection; Er:YAG laser; high-energy shock waves; hydrogen peroxide; methylene blue; photoc chemotherapy; root canal therapy +
Footnote †: journal: Applied Sciences
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 3 ISO/IEC 29192-2 Standard Lightweight Ciphers
### PRESENT Algorithm  
PRESENT with a 64-bit block length supports 80 or 128-bit keys. The algorithm is made up of 31 rounds and is based on the SPN architecture. Encryption algorithms based on the SPN architecture require fewer rounds, while maintaining robust security compared to those designed with the Feistel architecture. The PRESENT developers have raised the number of rounds since the block size has increased to 64-bit. The structure of the PRESENT encryption algorithm includes key addition, non-linear substitution, and linear bitwise permutation layers that make up each round. The PRESENT encryption structure is given in Figure 1b. The internal structure of PRESENT is designed for simplicity and efficiency, making it particularly suitable for resource-constrained environments. Each round in the SPN architecture consists of three key steps: key addition, substitution, and permutation. Key addition is performed by XORing the current round key with the data block, ensuring dependency on the key at each step. The substitution layer uses a 4-bit S-box, which is optimized for both hardware and security, to introduce non-linearity and protect against differential and linear cryptanalysis. Finally, the permutation layer reorders the bits of the block using a predefined permutation table, achieving a high diffusion rate with minimal computational cost. Additionally, the PRESENT key schedule is lightweight yet secure, generating round keys through simple rotations and XOR operations. This minimalistic design allows PRESENT to maintain strong security properties while requiring minimal resources, making it ideal for applications like the IoT and embedded systems.
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 4 Reeb Graph Sequential Algorithm on Triangulated Surfaces
### Outline of the Sequential Algorithm  
Now, we present the outline of the sequential Reeb graph algorithm. We assume that we are given a triangulated PL Morse function \(f:M\longrightarrow[a,b]\subset\mathbb{R}\) defined on triangulated mesh \(M\) without boundary. The case when the function \(f\) is not Morse, or when the \(M\) has a boundary, will be discussed in later sections.  
The sequential Reeb graph algorithm is given in the following steps:  
1. We start by sorting the critical points of \(f\) by their critical values. Let \(CP\) be the set of the sorted critical points of \(f\) in an ascending order.
2. For each critical point of the function \(f\), we define a node in the Reeb graph \(R(M,f)\). In other words, the node set of the graph \(R(M,f)\) precisely corresponds to the set of critical points of the function \(f\) defined on \(M\).  
Figure 4: For \(t_{p}\) a critical value, on the left, the set \(f^{-1}(t_{p})\) consists of a singular set \(C_{p}\) and two of simple closed curves. On the right, only the critical set \(C_{p}\) is shown, which is the connected component of \(f^{-1}(t_{p})\) that contains the critical point \(p\).  
Figure 5: A split saddle on the left and a merge saddle on the right.  
For each critical point \(v\) in \(CP\), we compute the critical set \(C_{v}\).
4. For each saddle or a minimum vertex \(v\) in \(CP\), we associate one or two ascending paths on the mesh: one ascending path if \(v\) is a minimum or a merge saddle, and two paths if \(v\) is a split saddle. For each ascending path, we march with until this path intersects with the first critical \(C_{w}\) set with a higher critical value than of \(t_{v}\). At this point, we insert an edge for the Reeb graph \(R(M,f)\) between the vertex \(v\) and vertex \(w\).  
Figure 6 illustrates the steps of the algorithm. It remains to describe two aspects of the previous algorithm: the construction of the critical sets mentioned in step (3) and the construction of the ascending paths that are mentioned in step (4).
# Scan Matching by Cross-Correlation and Differential Evolution
## 7 Conclusions  
A novel scan matching strategy, based on the combination of cross-correlation and differential evolution, was introduced in this work. It uses cross-correlation as an accurate, robust and computationally efficient measure of the similarity of 2-dimensional laser range scans and differential evolution for the search of scan transformation parameters. The proposed scan matching strategy was implemented and extensively evaluated. The evaluation used a software framework for the simulation of the movement of an autonomous mobile robot in an interior. The framework generated a data set composed of virtual laser range scans, taken when the robot followed a pre-defined trajectory in a virtual room.  
The proposed method was used to match laser range scans taken by the robot after it traveled different distances ranging from 75 mm to 3000 mm. The accuracy of the estimated translation and rotation of the robot was compared to the accuracy of translation and rotation obtained by a standard scan matching algorithm, ICP and a previous method based on a combination of RMSE and differential evolution. The experiments showed that the proposed method has only slightly lower accuracy than the ICP algorithm when matching laser range scans taken after the robot traveled between 75 mm and 750 mm but it achieved substantially better results for scans taken after it traveled 1500 mm or 3000 mm. The results of the computational experiments suggest that the proposed approach is an accurate and robust scan matching strategy suitable in particular for situations when the mobile robot travels longer distances.  
The future work will include an efficient implementation of the proposed method for embedded devices (for example, NVIDIA Jetson Nano) and field tests with a physical mobile robot in a real environment.  
Conceptualization, J.K., P.K., and P.M.; Methodology and Software, J.K. and P.K.; Validation, J.K., P.K., and M.P.; Formal Analysis, J.K. and M.P; Investigation, J.K. and P.K.; Writing--Original Draft Preparation, J.K., P.K., and M.P.; Writing--Review & Editing, P.K. and P.M.; Visualization, J.K.  
Figure 10: Absolute (**a**) and relative (**b**) rotation errors of scan matching experiments for laser range scans taken after robot movement of 75–3000 mm.  
**Funding:** This work was supported by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, project number CZ.02.1.01/0.0/0.0/16_019/0000867 within the Operational Programme Research, and also by the projects SP2019/107, \"Development of algorithms and systems for control, measurement and safety applications V\", SP2019/135, and SP2019/141 of the Student Grant System, VSB - Technical University of Ostrava.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 2 Method
### Feature Pre-Selection
#### 2.2.2 Cutting Vibration Features  
Cutting vibration has high correlation with tool wear. In this study, the cutting vibration features are extracted from cutting force signals for tool wear monitoring. To this end, a three-layer wavelet packet decomposition is used to decompose the cutting force fluctuation components from the original cutting force signal, as shown in Figure 3.  
Figure 3: Structure of the three-layer wavelet packet decomposition for reconstruction of cutting force signals.  
Figure 2: Interaction of tool and workpiece in milling process.  
Wavelet packet decomposition (WPD) is a time-frequency analysis method. In WPD, the scale function of a standard orthogonalization \(\psi(x)\) is used, and with two scale difference recursive Equations, the orthogonal wavelet packet is generated as  
\[\left\{\begin{array}{c}w_{2n}(x)=\sqrt{2}\sum_{k=Z}h_{k}w_{n}(2x-k)\\ w_{2n+1}(x)=\sqrt{2}\sum_{k=Z}g_{k}w_{n}(2x-k)\end{array}\right. \tag{5}\]  
where \(w_{0}=\psi(x)\), \(h_{k}\), \(g_{k}\) are, respectively, a pair of conjugate quadrature filter coefficients derived from \(\psi(x)\). For signal \(s(t)\), the discrete orthogonal WPD is defined as the projection coefficient of \(s(t)\) on the orthogonal wavelet packet base, which can be expressed as  
\[P_{s}(n,j,k)=\left\langle s(t),w_{n,j,k}(t)\right\rangle=\int\limits_{-\infty }^{+\infty}s(t)\Big{[}2^{-\frac{j}{2m_{k}}}\Big{(}2^{-j}t-k\Big{)}\Big{]}dt \tag{6}\]  
where \([P_{s}(n,j,k)]\) is the WPD coefficient sequence of \(s(t)\) on the orthogonal wavelet packet space \(U_{j}^{n}\). By setting a group of different conjugate quadrature filter coefficients of \(\{h_{k}\}\) and \(\{g_{k}\}\), the wavelet packet transform coefficients can be expressed as  
\[\left\{\begin{array}{c}P_{s}(2n,j,k)=\sum_{k=Z}h_{l-2k}P_{s}(n,j-1,l)\\ P_{s}(2n+1,j,k)=\sum_{k=Z}g_{l-2k}P_{s}(n,j-1,l)\end{array}\right. \tag{7}\]  
Then, the energy distribution of the reconstructed signal in the time-frequency domain is expressed as  
\[E(j,n)=\sum_{k=Z}[P_{s}(n,j,k)]^{2} \tag{8}\]  
In this study, the reconstructed force component with WPD caused by vibration is adopted as the input feature for tool wear monitoring.