esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Aiming to solve the issues of the high harvest losses and low harvesting efficiency of the wide-narrow-row corn harvesting platforms in China, a side-mounted header method was proposed, and a high-efficiency, low-harvest-loss, wide-narrow-row corn harvesting platform was designed.  
1. An energy balance model was established for the corn ears, which clarified that the change in kinetic energy before and after the collision between the ear and the picking plate is the main cause of inducing corn grain detachment and damage.
2. Single-factor experiments showed that the side-mounted header method can effectively increase the header length, thereby increasing the feeding angle and improving the feeding speed. This can reduce harvest losses and increase harvesting efficiency. Additionally, it validates the accuracy of the energy balance model for the corn ears.
3. Through response surface experiments, the analysis of the impact of plant feeding speed, stalk roller speed, feeding angle, and the interaction between some factors on the indicators was conducted. The optimal working parameter combination was determined to be a plant feeding speed of 1.08 m/s, a feeding angle of 52.46\({}^{\circ}\), and a stalk roller speed of 835.25 r/min, resulting in a grain breakage rate of 1.09% and a grain loss rate of 0.14%. Field trials confirmed a grain breakage rate of 1.12% and a grain loss rate of 0.14%. The design complies with relevant national standards and can meet the requirements for low-loss and efficient harvesting in wide-narrow-row corn planting areas.  
Conceptualization, W.D. and Y.W.; methodology, W.D.; software, W.D.; validation, X.Z.; formal analysis, Y.W.; investigation, J.Y.; resources, F.L.; data curation, W.D. and H.H.; writing--original draft preparation, W.D.; writing--review and editing, Y.W. and H.B.; the Author of the photographs included, W.D. and H.H.; visualization, W.D. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Inner Mongolia Autonomous Region Science and Technology Plan Project (2023 YFHH0012) and Key R & D project of Ordos City (2022 YY027).  
Not applicable.  
Data available statement: Data are contained within the article.  
Funding acquisition, [PERSON] supervision, [PERSON]; project administration, [PERSON].  
The authors declare no conflict of interest.
###### Abstract  
Anomaly prediction in time series is crucial for ensuring the stability and security of data centers, especially in scientific contexts such as INFN-CNAF, the National Center for Research and Development in Information and Communication Technology of the National Institute for Nuclear Physics. At INFN-CNAF, large volumes of heterogeneous data critical to international experiments are managed using dedicated monitoring systems. To ensure continuous availability, artificial intelligence solutions are being explored to detect anomalies and predict potential failures proactively. This work presents a machine learning-based approach for automatic anomaly prediction in the operational metrics of INFN-CNAF's WebDav service. We evaluate several methods, including Long Short-Term Memory, Random Forest, and various neural networks, assessing their Accuracy and sensitivity in distinguishing normal from anomalous behaviors. The results demonstrate the effectiveness of these methods, not only in predicting anomalies but also in pinpointing critical areas within monitored metrics. This contributes to more proactive IT resource monitoring and enhances data center management efficiency.  
a _Applied sciences_  
Academic Editor: [PERSON]  
12 December 2024  
Revised: 7 January 2025  
Accepted: 8 January 2025  
Published: 11 January 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Machine Learning-Based Anomaly Prediction for Practice Monitoring in Data Centers: A Case Study on INFN-CNAF. _Appl. Sci._**2025**, 15, 655. [[https://doi.org/10.3390/app15020655](https://doi.org/10.3390/app15020655)]([https://doi.org/10.3390/app15020655](https://doi.org/10.3390/app15020655))  
**Copyright:** 62025 by the authors.  
Liconsec MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods  
In analyzing the impact of variation in illumination on CIs and LAI estimation, we deviated from conventional approaches of studying CIs under different illumination conditions. This was because the usual methods do not align with the practicality of agricultural research, as UAV-based studies typically prioritize sunny weather conditions. During specific rice growth stages, the weather conditions may persistently remain overcast or partly cloudy. Therefore, we employed the automated multi-scale Retinex correction method to eliminate the influence of variable illumination on UAV images, resulting in corrected CIs that we named Retinex-corrected CIs. Then, we analyzed the changes in the original CIs and Retinex-corrected CIs. We investigated the variation in the relationship between CIs and LAI, assessed the response of CIs to variation in illumination, and examined the influence on LAI estimation. To assess the impact of the research background on CI and LAI, we segmented images into rice plants and background. The image segmentation adopted a method based on CIs and the Otsu threshold. To mitigate the impact of illumination on CIs that may result in significant errors in image segmentation, we opted for the Retinex-corrected CIs. We then proceeded to analyze the relationship between the Retinex-corrected CIs and LAI as well as the relationship between the Retinex-corrected CIs post-segmentation (segmented CIs) and LAI and examined how these relationships changed to evaluate the influence of the image background on LAI.  
The overall workflow of the research is illustrated in Figure 1. The method comprises three steps: (1) data collection, including the collection of field and UAV-based data; (2) data processing, including generation of orthoimages, calculation of CIs, illumination correction, and image segmentation; and (3) statistical analysis, including correlation and regression analyses.
# Recent Advances in Tunable External Cavity Diode Lasers
###### Abstract  
A narrow linewidth tunable laser source is a critical component in various fields, including laser radar, quantum information, coherent communication, and precise measurement. Tunable external cavity diode lasers (ECDLs) demonstrate excellent performance, such as narrow linewidth, wide tunable range, and low threshold current, making them increasingly versatile and widely applicable. This article provides an overview of the fundamental structures and recent advancements in external cavity semiconductor lasers. In particular, we discuss external cavity semiconductor lasers based on quantum well and quantum dot gain chips. The structure of the gain chip significantly influences laser's performance. External cavity quantum well laser has a narrower linewidth, higher power, and better mode stability. Conversely, external cavity quantum dot laser provides a wider tunable range and a remarkably lower threshold current. Furthermore, dual-wavelength external cavity tunable diode lasers are gaining importance in applications such as optical switching and terahertz radiation generation. With the continuous optimization of chips and external cavity structures, external cavity diode lasers are increasingly recognized as promising light sources with narrow linewidth and wide tunability, opening up broader application prospects.  
external cavity diode laser (ECDL); tunable laser; grating-feedback; F-P cavity; dual-wavelength 2020
## Appendix A General Description of Levels
\begin{tabular}{l l} \multicolumn{2}{l}{} & \multicolumn{1}{c}{**Table A1.**_Cont.} \\ \hline \hline
**Level** & **Level Description** \\ \hline \multirow{7}{*}{Level 2} & Internal logistics processes are fixed and managed. The process is defined through planning and implementation of best practices and management practices. The use of basic automated elements in standardized activities is beginning; however, mechanised methods prevail in cooperation with the worker. Data collection is partially digitised. Periodic data processing into various internal information systems that are not interconnected. Internal information systems, which are specifically profiled, have basic functionalities according to the sub-area of internal logistics and data are not structured in them. Basic connection of handling and storage technology, introduction of communication system. \\ \hline \multirow{7}{*}{Level 3} & Manipulation and storage technology is more automated for predefined tasks with human cooperation. Digitised technologies and information systems are used for data collection, which are also connected to external data sources, where mutual communication takes place. There is data processing into information systems that are interconnected. It is possible to automate processing locally, including automatic collection into data warehouses. It involves interconnection, transportation technologies and planning software; and partial automatic control of processes and technology using advanced IS. \\ \hline \multirow{7}{*}{Level 4} & Processes are digitised and automated, with limited human intervention, which usually acts as a controller. At this level, therefore, most processes are automated; however, it is not yet complex automation. The process is digitally oriented and based on a solid technological infrastructure. It uses information systems that connect all company areas, including external sources, and there is automatic processing. Information systems are shared across the enterprise. There is integration of structured and unstructured data; data processing from all areas; use of advanced predictive analytics; autonomous synchronization of production and logistics processes. \\ \hline \multirow{7}{*}{Level 5} & Processes are fully automated and only human-checked. It is therefore a complex automation, where over all process is automated and humans takes over only the role of planning and strategic management. The control of all systems is autonomous. Handling, storage and packaging technology is controlled by an in formation system. The company is fully equipped with sensors. Creation of a unified data model, and fully automated data processing are available. There are online communications and automatic information systems that are fully interconnected, compatible, interconnected with production systems and managing the entire logistics part of the company, including external resources. It involves comprehensive business coverage using wireless technology (optical fibre), low-energy technology; real-time data ex change, M2M communication based on control systems with AI. \\ \hline \hline \end{tabular}
## 3 Methodology
### Support Vector Machine (SVM)  
Its working is based on Statistics Learning Theory (SLT) and structure risk minimization principal (SRMP), it is considered a unique method in machine learning for signal processing. It has unique performance in solving nonlinear learning problems and sample learning problems [19; 20]. Based on optimization technology SVM is a unique tool in solving machine learning challenges. It has been first used by Vapnik in 1990's and now days they are used in pattern recognition in image processing and  
Figure 3: **(a)** MFCCs computation; **(b)** Decomposition tree of wavelets (3 rd level).  
signal processing. Based on SLT and SRMP, it can solve many complex structures, process big data appropriate to over learning [20]. The use of SVM has been started as hyper plane classifier and is useful in a situation where data is required to be separated linearly [21].  
Suppose having sample \(\mathrm{S_{i}}\) and its sorts \(\mathrm{T_{i}}\), expressed as (\(\mathrm{S_{i}}\), \(\mathrm{T_{i}}\)), \(\mathrm{S\in R^{d}}\), \(\mathrm{T_{i}\in(1,-1)}\), i = 1,..., N. The d is dimension number of input space. For standard SVM, its classifying border or edge is \(\frac{2}{||w||}\). Making the classifying borer or edge to maximum is equal to making the \(||w||^{2}\) to minimum. Therefore, the optimization problem of making the classifying edge to maximum can be expressed in the follow quadratic programming problem:  
\[\mathrm{w}\mathrm{b}\mathrm{e}\mathrm{J}\ (\mathrm{w},\ \varepsilon)= \frac{1}{2}\|w\|^{2}+\mathrm{C}\ \sum\
olimits_{i=1}^{N}\varepsilon_{i} \tag{3}\]  
Subject to  
\[\mathrm{T_{i}}\big{[}\mathrm{w}^{\mathrm{T}}\varphi(\mathrm{S_{i}})+\mathrm{b} \big{]}\geq 1-\varepsilon_{\mathrm{I}}\]  
\[\mathrm{i=1,\...,\ N}\]  
\[\varepsilon_{\mathrm{i}}\geq 0,\ \mathrm{i=1,\...,\ N}\]  
where \(\varepsilon_{\mathrm{i}}\geq 0\) (i = 1,..., N ) is flexible variable, which ensures classification validity under linear non-separable case, and parameter C is a positive real constant which determines penalties to approximation errors, a higher value of C is similar as to assigning a higher penalty to errors. The function \(\varphi\) is a non-linear mapping function, by which the non-linear problem can be mapped as linear problem of a high dimension space. In the transforming space, we can get optimal hyper plane. The SVM algorithm can discern right classification to sample by solving above quadratic programming problem [21]. We choose nonlinear kernel (quadratic SVM) model for our experiments, in that case SVM needs another parameter gamma along with C to be optimized for better results. Both of these values greatly affect the classification decision of SVM, larger values of gamma brings classifier closer to training accuracy and lower values make it away from it [22]. Similarly lower values of C are more suitable in bringing decision function closer to training accuracy and vice versa. In our experiments we achieved good training accuracy using C values in range of 0.0003 to 0.0001 and gamma value of log10\({}^{3}\).
# On the Thermal Stresses Due to Weathering in Natural Stones
## 2 Fundamentals and Methodology
### Inner Temperature  
The internal temperature was calculated assuming a simplified formulation in which the heat flux inside the slab occurs due to conduction only, as shown in Figure 2. Convective and thermal radiation are considered on the external side; on the internal side, just convective heat transfer is considered.  
In this formulation, the temperature at the internal face of the slab is given by the harmonics of the Fourier series and natural frequencies [28]:  
\[t_{ni}=t_{i}+\frac{1}{h_{i}}\big{[}U(\bar{t}_{e}-t_{i})+\sum V_{n}t_{e,n}\cos (\omega_{n}\theta_{t}-\psi_{n}-\Theta_{n})\big{]}, \tag{26}\]  
where \(t_{ni}\) is the inside wall surface temperature, \(t_{i}\) is the inside dry-bulp temperature, \(h_{i}\) is the combined convective and thermal radiation heat-transfer coefficient for the inside surface, \(U\) is the heat transmission coefficient, \(V_{n}\) is a factor, \(\bar{t}_{e}\) is the mean value of sol-air temperature, \(t_{e,n}\) is the harmonic coefficient, \(w_{n}\) is the angular velocity, \(\theta_{t}\) is the time, \(\psi_{n}\) is the angle, and \(\Theta_{n}\) is the lag angle.  
\[U = \frac{1}{\frac{1}{h_{i}}+\frac{1}{k}+\frac{1}{h_{0}}} \tag{27}\] \[V_{n} = \frac{h_{o}h_{i}}{\sigma_{n}k\sqrt{\gamma_{n}^{2}+Z_{n}^{2}}} \tag{28}\]\[\sigma_{n} = \sqrt{\frac{w_{n}}{2\alpha}} \tag{29}\] \[\Theta_{n} = \tan^{-1}\biggl{(}\frac{Z_{n}}{Y_{n}}\biggr{)}\] (30) \[\tan(\psi_{n}) = \frac{N_{n}}{M_{n}}, \tag{31}\]  
where \(h_{0}\) is the combined convective and thermal radiation heat-transfer coefficient for the inside surface, \(k\) is the thermal conductivity, \(L\) is the thickness of the material, \(Z_{n}\), \(Y_{n}\), and \(\sigma_{n}\) are factors, and \(N_{n}\) and \(M_{n}\) are the Fourier coefficients.  
\(Y_{n}\) and \(Z_{n}\) are defined as:  
\[Y_{n}=\biggl{(}\frac{h_{0}h_{i}}{2\sigma_{n}^{2}k^{2}}+1\biggr{)} \cos(\sigma_{n}L)\cdot\sinh(\sigma_{n}L)+\biggl{(}\frac{h_{0}h_{i}}{2\sigma_{n }^{2}k^{2}}-1\biggr{)}\sin(\sigma_{n}L)\cdot\cosh(\sigma_{n}L)+\] \[\biggl{(}\frac{h_{0}+h_{i}}{\sigma_{n}k}\biggr{)}\cos(\sigma_{n}L )\cdot\cosh(\sigma_{n}L) \tag{32}\] \[Z_{n}=\biggl{(}\frac{h_{0}h_{i}}{2\sigma_{n}^{2}k^{2}}+1\biggr{)} \sin(\sigma_{n}L)\cdot\cosh(\sigma_{n}L)-\biggl{(}\frac{h_{0}h_{i}}{2\sigma_{ n}^{2}k^{2}}-1\biggr{)}\cos(\sigma_{n}L)\cdot\sinh(\sigma_{n}L)+\] \[\biggl{(}\frac{h_{0}+h_{i}}{\sigma_{n}k}\biggr{)}\sin(\sigma_{n}L )\cdot\sinh(\sigma_{n}L) \tag{33}\]  
\[M_{n} = \frac{2}{T}\int_{0}^{T}t_{e}\cos(w_{n}\theta_{t})d\theta \tag{34}\] \[N_{n} = \frac{2}{T}\int_{0}^{T}t_{e}\sin(w_{n}\theta_{t})d\theta, \tag{35}\]  
where \(T\) is the period of the periodic function.  
The temperature at each point of the slab is calculated by interpolation of internal and external temperatures with respect to [PERSON]'s law of heat conduction.
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
## 4 Experiment and Result
The dual-AGV collaborative transport experiment systems and environment are shown in Figure 12.  
In this experiment, the two AGVs are set to move 7200 mm along a straight line, and the speed is set to 0.8 m/s. The formation angle \(\Theta_{0}\) is 90\({}^{\circ}\) and expected distance \(D\) is 1200 mm. Network parameters are shown in Table 1. In the formation algorithm, \(k_{1}=0.1\), \(k_{2}=0.1\), \(k_{3}=0.01\), and the formation control cycle is 16 ms.  
To eliminate the influence of network conditions, we repeated the experiment three times. As comparison, we tested the formation motion accuracy without formation control in the same kinematic parameters. The results of the experiments are shown in Figure 13.  
Figure 12: Experiments systems: (**a**) AGVs with measurement systems; (**b**) Experiment operation. Figure 13: Formation motion Error: (**a**) distance/mm; (**b**) angle/\({}^{\circ}\).
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 4 Solution Methodology
### Coordinated Optimization of Two Systems  
In order to realize the coordination between the two systems, two slack variables \(s_{\mathrm{GT},j,t}\) and \(s_{\mathrm{P2G},p,t}\) are introduced to, respectively, represent the infeasible part of gas-fired units consumption and P2G facilities production under the operation constraints of natural gas system.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 4 Acoustic Emission Source Localization Techniques for Three-Dimensional (3D) Structures
### Point Location Based on Arrival Time Approach
#### 4.5.5 Simplex Method  
The simplex method is a sound source localization method that searches for the minimum value of a mathematical function through function comparison [103]. The starting point of the simplex method is that for a given arrival time related to a set of sensors, the error at any point can be calculated by comparing the observed and calculated arrival times.  
The simplex method is an important source localization method. Due to its unique iterative method, divergence is impossible. This gives this method a significant advantage over derivative methods. Another major advantage of this method is its flexibility in handling complex velocity models. Unlike the derivative methods, which require the establishment of an arrival time function before analysis, the arrival time function used in the simplex method can be established during data processing, which is a necessary condition for dealing with many complex problems.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Author** & **Methods/Models** & **Material** & \begin{tabular}{l} **Number of** \\ **Sensors** \\ \end{tabular} & \begin{tabular}{l} **Performance Index** \\ **(Unit)** \\ \end{tabular} & **Value** \\ \hline Carpinteri 2006 [68] & Measuring time delays & RC beam & 5 & — & — \\ \hline Carpinteri 2012 [69] & Akaike Information Criterion (AIC) & Fiber RC beam & 7 & Location error (\%) & 3–4 \\ \hline Baxter 2007 [70] & “Delta T” source location & An aircraft component & 4 & Location error (\%) & 1.77 \\ \hline [PERSON] and Li 2012 [73] & Analytical method & Rod of granite rock & 5 & Location error (m) & 0.01–0.03 \\ \hline [PERSON] 2020 [74] & \begin{tabular}{l} Rapid acoustic source localization technique \\ \end{tabular} & \begin{tabular}{l} Aluminum cubes and \\ iron cubes \\ \end{tabular} & 4 & Mean error (\%) & 9.17 \\ \hline [PERSON] 2021 [59] & FastWay & \begin{tabular}{l} Reinforced concrete \\ beam \\ \end{tabular} & 12 & \begin{tabular}{l} Average location \\ error (mm) \\ \end{tabular} & 4 \\ \hline [PERSON] 2012 [92] & \begin{tabular}{l} The time reverse \\ modeling (TRM) \\ \end{tabular} & \begin{tabular}{l} Reinforced concrete \\ beam \\ \end{tabular} & 8 & — & — \\ \hline [PERSON] 2014 [93] & Ray-trace technique & \begin{tabular}{l} Numerical \\ investigations \\ \end{tabular} & 8 & Location error (\%) & 10 \\ \hline [PERSON] 2017 [94] & \begin{tabular}{l} The spatiotemporal \\ relationship equation of \\ acoustic emission source \\ \end{tabular} & Granite and iron & 4 & \begin{tabular}{l} Average absolute \\ error (mm) \\ \end{tabular} & \textless{}1.5 \\ \hline [PERSON] 2019 [95] & \
\begin{tabular}{l} APTRM combined with \\ ESM \\ \end{tabular} &
\begin{tabular}{l} A rectangular wooden \\ cabinet \\ \end{tabular} & 48 & Location error (m) & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three-dimensional sound source localization methods.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 2 Background
### Blockchain  
Blockchain is a shared and distributed ledger that can record transactions among untrusted peers [5]. This ledger is immutable and continually grows in a chronological order as new records, called blocks, are added holding the most recent transactions [6]. Each block contains, among others, transaction data, a timestamp and a hash of the previous block. The latter prevents alteration of data, as any attempt to modify data in a block would result in invalid hash values, unless all blocks' hashes are recalculated [7]. Blockchain is typically divided by the National Institute of Standards and Technology (NIST) [8] into two main types: _permissioned_ (private), where participants need permission from authority to join the network and _permissionless_ (public) where anyone can freely participate.  
The benefits of using blockchain in IoV are numerous, because blockchain technology has the following main features:  
* Immutability: Tampering or deletion of block's data is not feasible, as even a minor change in the block's data will alter its hash value and, as a result, the hash value of every subsequent block in the chain. Therefore, any modification to an old block's data would typically require replacing all following blocks.
* Distributed Ledger: Participating nodes can keep a copy of the ledger, which can facilitate trust establishment among them.
* Consensus Algorithm: Mechanism that is used to provide rules for verifying and validating blocks, as well as for ensuring that all participants agree with the current state of the chain.
* Smart Contract: A piece of code that executes automatically when certain conditions are met and the execution result must be agreed upon by all participants.  
Considering all the above characteristics, blockchain technology can be used to solve security, trust establishment and distribution problems which may occur in vehicular networks.  
Figure 1: The IoV Ecosystem.