esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Anomaly Forecasting
### The Structure of Network Anomaly Forecasting
#### 4.1.1 Network Data Prediction  
In pursuit of forecasting anomalies in unlabeled network data, we tailored our approach to primarily focus on two crucial aspects. These elements reflect our strategic utilization of available historical data and the application of advanced predictive network modeling techniques. As we dive into these sections, we will be shedding light on the in-depth processes and methodologies used in our study.  
The historical data of 5G vertical applications with length \(T\) is utilized as input, where each certain time \(t\) contains \(M\) kinds of features. These input data provide us with a rich history of network performance that can be used to forecast future network activity. We predict future time series data of length \(T_{0}\) from historical data, in which \(T_{0}<T\). This prediction allows us to project future network performance based on past performance and current network conditions. Once we have obtained the predicted data, we divide it into multiple time windows. This allows us to examine the network performance in discrete, manageable chunks, rather than as a continuous stream of data. Each time window represents a unique snapshot of network performance.  
In the context of predictive network modeling, LSTM is a type of recurrent neural network (RNN) that has shown excellent performance in sequence prediction tasks due to its ability to retain information over a longer period of time. LSTM is composed of multiple memory blocks, each of which has a cell state and a hidden state that are used to generate the output related to the input.  
At each time step \(t\), the matrix of historical indicator data feature \(X_{t}\) is fed into the LSTM network. The network then generates the cell state \(C^{t}\) and hidden state \(h^{t}\), which provide the LSTM network with a sort of \"memory\" of past network conditions. The previous cell state \(C^{t-1}\) and hidden state \(h^{t-1}\) are also utilized to compute \(h^{t}\), allowing the network to retain information over time. This temporal memory is one of the key features that make LSTM networks so effective for time series prediction tasks.  
In addition, LSTM memory blocks iteratively update themselves through the gating signals from the three gate controllers of the forget gate, input gate, and output gate. Each of these gates serves a unique function in the LSTM network: the forget gate determines which information to discard from the cell state, the input gate determines which information to update in the cell state, and the output gate determines which information to output as the final prediction.  
Once we have obtained the predicted data, we conduct anomaly detection using the method introduced in Section 3 to achieve network anomaly forecasting.
## 5 Numerical Evaluation Results
### Results
#### 5.2.1 Temporal Spacing of Operator Peak Demands
Subsequently, the queue length decreases down to zero over 20 s as effectively an \"extra\" backhaul bitrate of 5 Mbps, i.e., the allocated 10 Mbps minus the currently served base load of 5 Mbps, is serving the backlog from 20 s to 40 s simulation time.
## 1 Introduction  
Beam elements of structures are widely used in engineering practices. They may contain cracks that are powerful stress concentrators, decreasing the reliability and durability of such structures.  
Under external load, crack faces may be in contact. Many researchers have studied plane contact problems in crack theory of homogeneous bodies and developed methods for solving this problem. They are: Mosakovskyy V.I., Zagubizhenko P.A. [1, 2], Bojko L.T., Berkovych P.E. [3], Grylitskyy N.D., Kit G.S. [4], Grylitskyy D.V., Lytsyshyn R.M. [5], Kryvcun M.G., Grylitskyy N.D. [6], Lozovyy B.L., Panasjyk V.V. [7, 8], Savruk M.P. [9], Fishtryskyy L.A., [PERSON]. [10], Bowie O.L., [PERSON]. [11], and [PERSON], [PERSON] [12].  
There are lots of various fracture criteria for cracked bodies in scientific literature. Several of them are of significant interest [13, 14, 15, 16, 17, 18, 19].  
The problem of bending of a cantilever-cracked beam by concentrated force applied at its end, perpendicular to its axes, is investigated in the papers [7, 8]. It was assumed that the crack is straight, through-thickness, perpendicular to beam axis, and its faces are particularly contacting. Using the theory of complex variable functions and complex potentials, the considered problem was reduced to a linear conjunction problem, and to a singular integral equation in unknown contact stresses.  
The complex potentials of the problem, the length of the contact area of crack faces, and the ultimate value of the force responsible for beginning of crack propagation were determined.  
A similar problem for nonsymmetrical cracks was studied in this paper. Depending on the location of a crack, its faces may be in perfect contact, particular contact, or have no contact.  
On the base of energy [15; 16] and improved [17; 18; 19] criteria, an ultimate value causing beam fracture was derived.  
Mark meaning are listed in Table 1 to improve understanding article by reader's.
## 5 Conclusions  
This paper proposes a reflective region removal method based on the combination of image complementation and illumination compensation, which uses illumination compensation to keep the brightness of the reflective region stable in real-time during the reflective region extraction, and eliminates the process of ellipse region fitting and the SIFT or ASIFT extraction of feature points via fast image region matching, combining the MSER-based fast image region matching method with the illumination compensation mechanism. The combination of the MSER-based fast image region matching method, illumination compensation mechanism, and image complementation-based reflective region removal method reduces the spatial and temporal complexity of the reflective region of the vehicle's front windshield and improves the speed and accuracy of reflective region extraction. In the reflective region removal process, the following occur: re-extracting video frame image sequences, forming custom image sequences, performing illumination compensation for the reflective region mapping in each image, keeping the illumination intensity of the reflective region of the custom image sequence consistent, restoring the reflective region covered with the background layer by combining the non-reflective region of each image with the same reflective region, and removing the reflective region using image difference. The video image sequence containing the reflected region is finally removed with image difference, and the process clearly illustrates the feasibility of the detection method combining image complementation and illumination compensation. In order to solve the problem of the incomplete extraction of reflective regions caused by real-time luminance changes, this paper uses the combination of luminance adjustment and gamma correction in the process of light compensation to improve the contrast of the deep and shallow parts of the image by setting the gamma function, which improves the problem of the incomplete identification of reflective regions with luminance adjustment only. The reflective region identification and removal process of the reflective region removal method are based on the combination of image complementation and illumination compensation and are illustrated through real-vehicle tests. The reflective region removal method based on image complementation, the reflective region removal method based on relative motion, and the proposed method are compared in a real vehicle test outdoors, and the test results are compared using DT, RT, and NCC as evaluation indexes. The results show that the method has high accuracy, and the reasons for the high accuracy are analyzed. The reflective area removal method based on image complementation, the reflective area removal method based on reflective area removal, and the reflective area removal speed of the method were compared; the results showed that the method has a faster processing speed, and the reasons for the faster processing speed were analyzed. However, the method is slow in decision making, which may lead to the problem of time delay in the reflected
# Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis
## 3 Methodology for Flat Detection
### Envelope Spectrum Approach to Detect a Wheel with Spectral Kurtosis Analysis
The idea of this step is to define three additional bandpass filters \(g\) with frequency bands [0 \(\frac{1}{6}\)], [\(\frac{1}{6}\) \(\frac{1}{3}\)] and [\(\frac{1}{3}\) \(\frac{1}{2}\)] of the sampling frequency, as follow: \[ \begin{array}{l}g_{1}=ge^{j\pi/6}\\ g_{2}=ge^{j3\pi/6}\\ g_{3}=ge^{j5\pi/6}\end{array} \] (8)  
These filters are then used to further decompose each sequence \(R^{i}_{l}(t)\) into three subsequences, regarding the low, medium, and high frequencies. After decomposition, the kurtosis of all these sub-sequences is computed according to Equation (5). 4. After filtering with the above low-pass and bandpass filters, each signal obtained from section (ii) is taken into account as the main signal (\(R^{i}_{l}(t)\)) and this process is iterated from \(l=0\) down to level \(l-1\). The kurtogram would finally be estimated by computing the kurtosis for all sequence signals. The framework to obtain the kurtogram is depicted in Figure 7. Moreover, the center frequency and bandwidth frequency for the level with the highest kurtosis can be obtained by the following equations [60; 61]: \[\Delta f=2^{(-l-1)}F_{s}\] (9) \[f_{o}=(i+0.5)\Delta f\] (10) where \(\Delta f\) is the bandwidth, \(F_{s}\) is the sampling frequency, and \(f_{o}\) is the center frequency. Figure 7(a) shows a signal contaminated with noise obtained from position 9, considering layout scheme 1 (see Figure 5). Figure 7(b) presents the kurtogram using the above framework for the mentioned signal corresponding to the Alfa Pendular train. As shown in Figure 8, the maximum kurtosis is obtained as 60.25 at level 3, with the corresponding band frequency equal to \(\left[\frac{1}{16}\ \frac{1}{8}\right]\). Therefore, considering Equations (9) and (10), the center and bandwidth frequencies for the corresponding signal are calculated as follow:  
\[ \begin{array}{l}\Delta f=2^{(-3-1)}\times 2000=125\ \mathrm{Hz}\\ f_{o}=(1+0.5)\times 125=187.5\ \mathrm{Hz}\end{array}  \tag{11}\]  
where the sampling frequency (\(F_{s}\)) is 2000 Hz, the bandwidth frequency (\(\Delta f\)) is 125 Hz and the center frequency (\(f_{o}\)) is 187.5 Hz. More details regarding the envelope spectrum approach for wheel flat detection are presented by [PERSON] et al. [37]. In the following sections, the envelope spectrum approach is employed to detect a wheel flat. For this purpose, spectral kurtosis is used to calculate the center frequency and bandwidth for each signal as a key preprocessing step before performing the envelope spectrum. The sensitivity of the layout schemes (presented in  
Figure 7: Framework to obtain the kurtogram, the central frequency, and the frequency band for each signal. Figure 5) is verified according to the type of sensors, the type of train, and the position of the sensors. Sensitivity of Layout Schemes to the Unevenness Profile of the Rail and the Different Types of Trains
## 2 Experiment Scheme
### Test Specimen
#### 2.2.3 Data Acquisition and Monitoring Device  
When the hydraulic system is working, the internal particles of the hydraulic oil collide with each other and produce violent friction. Hydraulic oil is also subject to friction resistance when passing through local obstacles. It causes the loss of oil in the oil circuit and the reduction of oil pressure, ultimately consuming the energy of the whole system. For the resistance in the oil circuit, there is an error between the load applied on steel wire and provided by FTM. A force sensor is used to monitor the actual load acting on steel wire and adjust it to the value set by FTM. Considering that the wire is circular, a hollow shaft type annular force sensor, named JHBH, is selected to monitor the fatigue loads, as shown in Figure 7. The working principle of this sensor is to convert the force received by the steel wire into the voltage signal. A data acquisition device, named NATIONAL INSTRUMENTS BNC-2111, is used to collect the voltage signal output from the sensor, as shown in Figure 8. The JHBH is connected to the BNC-2111 by transmission data line, and then to a computer. The computer records and reads the tension in each wire during fatigue loading. LabVIEW programming language [25] is used to calibrate the collected signal, to realize the conversion of the voltage signal to force signal and perform the monitoring and recording of fatigue load. Figure 9 demonstrates the process and working principle of data collection by LabView software, through which force loads applied on each steel wire can be collected synchronously. LabVIEW uses the graphical editing language G to write the program in block diagram form. Figure 10a,b shows the fatigue loads recorded by LabVIEW software, that is, the real-time forces applied to multiple steel wires and one of the wires, respectively.  
Figure 8: Acquisition device—NATIONAL INSTRUMENTS BNC-2111.  
Figure 7: Hollow shaft type annular force sensor—JHBH.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges
### Identification of Mixed Cultures  
MALDI-TOF MS is optimized for the analysis of pure microbial isolates. When multiple organisms are present in a single spot, the resulting spectrum becomes a composite of overlapping signals, making accurate identification difficult or impossible. This limitationposes challenges in polymicrobial contamination events, spontaneous fermentations, or environmental monitoring samples where co-colonization is common [29; 103; 118].  
Although some advanced data deconvolution algorithms and software enhancements aim to identify dominant species in mixed spectra, their accuracy and routine utility are still limited. For mixed populations, pre-isolation steps, such as selective culturing or physical separation (e.g., flow cytometry), are generally required [30; 120; 141; 142].
## 1 Introduction
Moreover, mobile edges 'perform' as borders and control the flow management with SDN controllers. Mobile edges interact with the control plane to keep the latest information of the multimedia traffic and network status. Accordingly, the controller fetches the information from the control plane to know the exact situation of the network and helps to manage the network resources efficiently in a centralized manner. Moreover, the controller and switches utilize a low-cost secret sharing scheme to cope with information privacy and identify the uncertain multimedia traffic, increasing the efficacy of the communication system. The proposed model not only provides higher bandwidth for large size media data using mobile edges, it also protects the network data against anonymous behaviors. The three main contributions of the proposed model are as follows:  
1. It offers a learning approach, with a node prediction-based multimedia algorithm by exploring the mobile edges; it attains high delivery performance with efficient management of network bandwidth. 2. It offers a low-cost computation algorithm for constraint resources, with the integration of SDN technology and boundary edges for reducing the response interval, and delays constraint multimedia applications. The multimedia traffic protects against different interference attacks and centralizes the detection mechanism by increasing the support of the network applications. The rest of the paper is as follows: Section 2 presents the related work and limitations of the existing solutions. Section 3 introduces and discusses the proposed model. In Section 4, we present the numerical analysis and results. We conclude the research work in Section 5.
## 5 Technical Challenges and Ethical Issues
### Key Challenges and Requirements
#### 5.1.4 Privacy and Security  
Data privacy and security are critical in DT adoption, as healthcare data are highly sensitive and susceptible to breaches. Protecting individual data requires strict adherence to regulations and guidelines, along with robust cybersecurity measures [47]. Addressing these concerns is paramount to gaining trust and ensuring compliance with legal standards.
## 3 Results and Discussion
### Chemical Treatment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \(\mathbf{-14}\)\({}^{\circ}\)C & \(\mathbf{-16}\)\({}^{\circ}\)C & \(\mathbf{-18}\)\({}^{\circ}\)C & \(\mathbf{-20}\)\({}^{\circ}\)C & \multicolumn{2}{c}{\(\mathbf{-20}\)\({}^{\circ}\)C} \\ \hline  & \(\mathbf{mean\pm 495\%}\) & \(\mathbf{NR}\) & \(\mathbf{mean\pm 495\%}\) & \(\mathbf{NR}\) & \(\mathbf{mean\pm 495\%}\) & \(\mathbf{NR}\) & \(\mathbf{mean\pm 495\%}\) & \(\mathbf{NR}\) \\ \hline Control & \(0.69\pm 0.06\) & 64 & \(0.55\pm 0.09\) & 52 & \(0.34\pm 0.08\) & 55 & \(0.25\pm 0.08\) & 62 \\ Bunker 0.5 & \(0.70\pm 0.25\) & 13 & \(0.69\pm 0.15\) & 20 & \(0.53\pm 0.16\) & 16 & \(0.41\pm 0.15\) & 14 \\ Bunker 1.5 & \(0.91\pm 0.07\) & 10 & \(0.78\pm 0.20\) & 5 & \(0.73\pm 0.18\) & 3 & \(0.58\pm 0.27\) & 4 \\ \hline Bunker 0.5; 16 kHz/1.5 kV & \(0.90\pm 0.05\) & 5 & \(0.53\pm 0.18\) & 5 & \(0.20\pm 0.18\) & 9 & \(0.12\pm 0.13\) & 10 \\ Bunker 0.5; 5 kV/cm 1 min & \(0.52\pm 0.45\) & 3 & \(0.04\pm 0.03\) & 4 & \(0.01\pm 0.02\) & 4 & \(0.01\pm 0.01\) & 6 \\ Bunker 0.5; 16 kHz/1.5 + 5 kV & \(0.87\pm 0.13\) & 2 & \(0.62\pm 0.54\) & 3 & \(0.11\pm 0.18\) & 3 & \(0.12\pm 0.14\) & 4 \\ \hline
4.4 kHz/1.5 kV & \(0.83\pm 0.11\) & 7 & \(0.47\pm 0.26\) & 7 & \(0.39\pm 0.19\) & 7 & \(0.29\pm 0.24\) & 6 \\
4.4 kHz/2.4 kV & \(0.57\pm 0.13\) & 23 & \(0.50\pm 0.09\) & 28 & \(0.41\pm 0.12\) & 32 & \(0.36\pm 0.11\) & 36 \\
16 kHz/1.5 kV & \(0.75\pm 0.11\) & 27 & \(0.56\pm 0.15\) & 15 & \(0.35\pm 0.12\) & 25 & \(0.31\pm 0.13\) & 27 \\
16 kHz/2.4 kV & \(0.74\pm 0.18\) & 7 & \(0.80\pm 0.17\) & 9 & \(0.53\pm 0.35\) & 7 & \(0.48\pm 0.35\) & 8 \\ \hline
5 kV/cm 1 min & \(0.88\pm 0.09\) & 9 & \(0.80\pm 0.19\) & 13 & \(0.50\pm 0.19\) & 13 & \(0.51\pm 0.21\) & 15 \\
5 kV/cm 10 min & \(0.69\pm 0.15\) & 11 & \(0.47\pm 0.22\) & 11 & \(0.38\pm 0.19\) & 13 & \(0.40\pm 0.19\) & 14 \\
16 kHz/1.5 kV + 5 kV & \(0.59\pm 0.16\) & 11 & \(0.64\pm 0.15\) & 16 & \(0.63\pm 0.19\) & 17 & \(0.51\pm 0.15\) & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relative frost resistance of two-stage hardened seedlings after various seed treatments. \(\mathbf{mean\pm 495\%}\) indicates the mean value with 95% confidence interval, \(\mathbf{NR}\) indicates the number of repetitions. Designations of variants coincide with designations in Tables 1 and 2. The action of the disinfect had the expected decontaminating effect. The results of measuring the contamination of seeds with fungi in the control variant and the variant treated with the disinfect are shown in Table 6. If the main route through which the gas discharge affects germination is a decrease in contamination, then, in the variant with the combined action of the disinfectant and the discharge, the efficiency of the gas-discharge treatment should decrease.
## 7 Applications in SDN-based VANETs
### Stop Movement Signal Assistant  
These types of programs and applications are utilized to caution drivers to not cross any crossing point or generally to prevent any hazardous circumstance from happening. It includes the correspondence among the vehicular sensors and the SDN-RSUs. This type of application advises the driver that different vehicles are closer to the crossing point, so he or she needs to sit tight for quite a while. After the other vehicles cross the junction, the vehicle gets a green signal to cross the junction.
## 3 Methods and Data  
Figure 1 shows a conceptual scheme of the proposed approach, which starts with the collection of data on actual construction works.  
Figure 1: Conceptual scheme of the proposed approach.  
The approach presented in the paper involves a series of steps that integrate both quantitative and qualitative analyses of construction work efficiency. To make the analysis of the actual (real-life) construction works efficiency possible, data collection is necessary.  
In terms of quantitative analysis, it is sufficient to record data that are directly related to efficiency, such as the work output, number of hours spent, and resources used in executing a particular construction work. Based on these data, efficiency can be computed using various measures, but in this study, it is defined as the work output per unit of time (hour of work). This quantitative approach enables probabilistic analysis of construction work efficiency, leading to computations of either the duration or output of construction works, along with an assessment of the probability for a given case.  
To obtain a more comprehensive view and deeper insights, additional information about the conditions, circumstances, organisation, and characteristics of construction works must also be recorded. Such information is particularly crucial for qualitative analysis and the application of machine learning tools. The qualitative approach follows probabilistic analysis and is designed to enable the recognition of whether, under certain circumstances, the construction work efficiency being considered will be above or below a certain critical value.
## 5 Security Mechanisms
PUF produces a different key (Response) when different input (Challenge) is fed to the device, which is due to manufacturing variation of the chip [206,207]. Now, the characteristics of PUF will be discussed. Figure 11 depicts the characteristics of PUF [208,209]. Figure 11: Characteristics of PUF. Figure 10: Hardware-based security mechanisms. * Uniqueness: It is required to have different responses for each challenge. It is determined by how distinct one chip is from another in terms of strings and ultimately process variation of PUFs. Hamming distance (HD) calculated with each response represents uniqueness. The number of bit differences between the two responses calculated the HD. Ideally, uniqueness should be 50%. * Randomness: It is calculated based on the total count of \"1\"s and \"0\"s of a response. In response, it is expected that the presence of an equal number of \"1\"s and \"0\"s. If so, the PUF will have 100% randomness. * Correctness: A PUF needs to generate the same CRP irrespective of external variables such as temperature, pressure, time, etc. In every operating condition, the ideal value is 100%. * Reliability: A PUF, as the name implies, should be 100% reliable. Every time a challenge is presented, an ideal PUF will produce the same response. * Uniformity: This measures how random the PUF is. The probability of \"1\"s and \"0\"s should be equal so that any intruders are unable to guess. * Bit Aliasing: How a particular response bit across several chips are measured by bit aliasing, and the ideal value is 50%. * Steadiness: All the responses should be identical when the same challenges are feeding to a PUF. Ideally, the value of intra-HD should be 0%. PUF includes ring oscillator PUFs, Arbiter PUFs, Optical PUFs, SRAM PUFs, DRAM PUFs, and MRAM PUFs, among others. Blockchain is a distributed, immutable, decentralized, and shared digital ledger that can be used in IoT and IoE networks to provide security [210; 211]. There is no centralized entity to govern the data, and it is a peer-to-peer connection, unlike other systems. All nodes in the network come to a consensus to verify transactions and store the data in a block with a timestamp after the completion of the mining process by the miner node. The blocks create a chain which is called blockchain, and it uses SHA-256 and ECC for data integrity and authentication. Figure 12 shows the basic structure of a block in blockchain [212; 213]. Each block stores timestamp, transactions, and previous block hash along with others' attributes. It is very tough to alter data in blocks as each block is connected by a hash.