esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 5 Research Methodology
### Description of Datasets  
This system implements experiments using the datasets from the UNSW NB2015, the NSL KDD, the CIC_IDS2017, and the CICDDOS2019. The initial data collection is NSL-KDD. It was developed as a strong preprocessing stage (the first step of the reimplemented system) to decrease the forecasting challenges. Different baseline classifiers were used to categorize records with five different levels of complexity. Next to each occurrence, the number of accurate predictions was noted [80; 81; 82].  
The proportion of records picked for each difficulty degree category in the first KD-DCup'99 dataset is inversely related to the number of records chosen. In our sample, we used 125.973 occurrences of the KDD Train set, which included 58.631 attacks and 67.342 instances of routine traffic. Many current low-key assaults were included in the second dataset (UNSW-NB15), which aimed to replicate current network configurations. It contained 45 columns (id = 1, features = 44), 2,540,045 records spread over four big-data CSV files, and records for testing a total of 82,334 and training a total of 175,342, respectively [83].  
The CIC IDS2017 contained current large attacks [81] and benign data in addition to the results of the network traffic analysis using the CIC flow meters. Flows were used to time stamp protocols, IP addresses at the source and destination, ports, and all attacks. The most recent dataset also included updates for threats such as port scanning, DDoS, brute force, XSS, SQL injection, penetration, and botnets. A total of 2,830,744 records were spread among 8 files, each containing 78 unique named features [84].
## 8 Presentation of Results
### Presentation of Failure Prediction Model Results, Comparisons and Discussions
#### 8.1.2 Development of the Failure Prediction Model by Using Time Series  
Long Short-Term Memory (LSTM):  
For the purpose of predicting RuI, LSTM was defined with 50 neurons in the first hidden layer and 1 neuron in the output layer. One time step with all features were the input shape. As the loss function, the mean absolute error (MAE) function and the Adam efficient  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SVM** & **Naïve Bayes** & **Logistic Regression** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the prediction of the degradation state (example of cluster 1) for the three algorithms.  
version of stochastic gradient descent were used. The model was fitted to 500 training epochs with a batch size of 4000.  
Figure 14 shows the results found by using RUL B for the discrete cluster 1 and 2 failure type.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Decision Tree** & **Random Forest** & **SVM** \\ \hline \multirow{6}{*}{X = 0 h} & tens. between 1.0 & tens. between 1.0 \\  & the systems (see systems (see systems (see systems (see systems (see systems (see systems (see systems (see systems (see systems (0))))))))))))))))))))))))(((((((  
From the figures above, we see that RULA was well predicted compared with RULB. This shows that LSTM is clearly powerful for temporal analysis as well as sequential data. We tried to change the fixed value from RULA= 350 to RULA= 219, and we found a good fit with both continuous and discrete attributes. Figure 18 shows an example of continuous cluster 2 with RULA= 219.  
MSE = 5.354978290025313  
Figure 16: LSTM method for cluster 2 continuous-RULA. Mean squared error = 61.9745811938061.  
Figure 17: LSTM method for cluster 3 continuous-RULA. Mean squared error = 99.01424394693683.  
Final choice for the prediction: The RULA method with LSTM.  
A machine learning model can only begin to add value to an organization when the knowledge from that model is regularly made available to the users for whom it was designed. The process involves taking a trained machine learning model and making its predictions available to users or other systems.
# Online Learning Based Underwater Robotic Thruster Fault Detection
## 4 Propulsion System Online Modeling
### Multi-Center Particle Swarm Optimization Algorithm with Memory Ability  
For optimization problems that cannot obtain a global model, the swarm intelligence algorithm has obvious advantages over other methods. The PSO algorithm is one of the most recognized achievements in the field of swarm intelligence. It has a simple structure, is easy to implement, and does not rely on the characteristic information of the problem [41]. It has been widely studied and applied. In this paper, the model order and modeling data volume optimization problem cannot be described with a global model and the optimization process needs to be performed online; therefore, the PSO algorithm is used to solve the problem.  
The standard PSO algorithm has the disadvantage of easily falling into local optima. To solve this problem, researchers have proposed a variety of improved algorithms. The strategies adopted include optimizing the initial population, changing parameter values, changing the topology, introducing mutation operations, second search optimization, joint optimization with other swarm intelligence algorithms, and so on. Among these, optimizing the initial value can increase the possibility that the initial value falls into the global optimal interval. Changing the parameter value can speed up the convergence speed and optimize the search behavior. Both of these strategies can improve the performance of the algorithm without significantly increasing its complexity. Changing the topological structure can limit the particle's perception of the optimal value of the current group. Although it can avoid the particles falling into local optima, to a certain extent, it may also seriously affect the search efficiency. Introducing a mutation operation is equivalent to replacing some of the particles trapped in local optima with new particles, and then searching again. At this time, a large number of searches are generally required and the results of the new search are uncertain. Second search optimization assumes that the particles have a known evaluation function--that is, the global model--which violates the original intention of the algorithm. The joint optimization algorithm requires multiple searches, and the algorithm complexity is significantly higher than the original algorithm.  
The online optimization problem in this article requires the optimization algorithm to be as simple and easy to implement as possible, as well as to obtain better results in a shorter computing time. Based on the existing research results, we propose a multi-center PSO algorithm with memory capabilities. Its main strategies include initial population optimization based on historical optimal solutions and improved chaotic mapping, and the use of a multi-center collaborative search.
## 4 Comparison and Discussion
### Cognitive Radio Approach  
Cognitive radio is a technique that can sense and access a channel as a secondary user. When a WBASN works in an interference scenario, smart channel selection combined with a suitable MAC is needed. Cognitive radio-based WBASN can improve system performance and avoid interference. With the cognitive radio scanning process, spectrum status is estimated, giving WBASNs more flexibility to change their operation.  
In the adaptive cognitive radio MAC (CR-MAC) for HCV [39], the sensing process is performed before the coordinator starts its transmission; therefore, packets do not collide. The accessing time is decided at the coordinator after it calculates the collision rate based on the received RSSI. The Contention Window value is set by the priority of the traffic, reducing the collision rate and thereby improving the throughput of the system. In this case, the idle listening in the sensor nodes may cause a long end-to-end delay and high power consumption.  
The algorithm in FDCR [38] performs the same channel sensing operation as CR-MAC [39]. The RSSI is an important value to compare with the given threshold value, and the result is indicated to the idle spectrum or busy spectrum. One disadvantage of this scheme is that if beacon collision occurs and the WBASN senses the busy channel, the halted transmission may lead to a large end-to-end delay.  
Unlike adaptive CR-MAC [39] and FDCR [38], the cognitive framework of C-RICER [40] aims to switch the operating WBASN to another channel. This is more effective because the interference can be mitigated and the WBASN operation is continuous without halting [39]. Nevertheless, the QoS constraint is not considered and the construction of an interference map may cause a long delay. Similarly, the framework in [41] performs channel hopping based on the detection of an overlapping interference region.  
CR-WBASN is the model used for medical applications that requires a cognitive radio protocol for spectrum sensing [42,43]. The cognitive radio approach is considered as a promising technique to avoid both inter-WBASN and inter-domain interference. However, more research must be conducted for protocols in this model on such challenges as developing a MAC protocol for CR-WBASN that works with the IEEE 802.15.6 standard physical layer. A comparison of these cognitive radio approach schemes is presented in Table 3.
## 4 Simulation Results  
In this section, system performances of the equalization schemes and channel estimation schemes will be evaluated over different channel emulation scenarios. We perform simulation by MATLAB 2018b on the Intel(R) Core(TM) i7-5930 K CPU under 3.5 GHz and 64-bit Windows 7 operating system. One shot of the COST 207 typical urban (TU) channel delay profile with six taps is used in all simulation scenarios with a multipath channel [38]. The experimental system specifications are shown in Table 1.
# Application of Artificial Neural Network to Somatotype Determination
## 3 Results
All somatotypes (Eto, Endo and Meso) can be calculated using MLP 2-4-3 (R = 0.88, RMSE = 0.67, and \(\chi^{2}\) = 0.51). It should be noted that the ectomorphy is be calculated on the basis of height and weight and, therefore, it does not require further simplifications. Some limitations in this study should be considered. Firstly, our results have an application to multi-frequency BIA and could be differ for other devices. There are several manufacturers of BIA, using different algorithms, which is the main disadvantage of BIA devices. Secondly, our findings could be generalized for young females. Further studies are needed to consider ANN-BIA-based formulas as an alternative tool for somatotyping. The strengths of our study are that anthropometric and BIA measurements were carried out after one another. This allowed us to reduce the bias caused by the changes in body composition. Neural networks applied in our study are non-linear systems, which make it possible to classify the data better than existing linear model methods such as the aforementioned BIA-based formulas. Moreover, ANNs are not programmed, but are trained to do so in the examples [25]. The application of ANNs on BIA-data to determine the somatotype seems to be more accurate. These types of new technique are becoming increasingly attractive in health sciences, thanks to their ability to process huge amounts of data, suggest new correlations between markers obtained by different methods, and also lead to the possibility of a more precise diagnose and personalized treatment of the patient [24].
## 3 Experimental Activities
### In Situ Impact Sensors  
Hypervelocity impact testing can be also employed to assess the response of in-situ impact sensors for space applications [49,50]. In this context, the Space Debris group of the University of Padova was involved in the CADETS (A Calorimetric Detection System for Hypervelocity Impacts) project in collaboration with the University of Oxford and the University of Malta. This activity used Thin-Film Heat-Flux Gauges (TFHFG) to measure the local increase in shield temperature following an impact event, which is correlated with the kinetic energy of the debris. The experimental campaign was performed at the University of Padova and consisted of 4 impact tests on aluminum targets instrumented with a sensor based on TFHFG technology. Results showed contributions in sensors reading from both thermal and mechanical loads, with the latter predominant. More information can be found in [49].  
Figure 11: Comparison of experimental bi-dimensional characteristic length fitting lines for fragments larger than 1 mm (red dash-dot line) and in the range between 1 and 10 mm (solid red line) [47].  
Figure 10: High-velocity camera frames from the impact video: from left to right, projectile approaching the target, collision, and impact flash [47].
# A Scalable Bidimensional Randomization Scheme for TLC 3D NAND Flash Memories
## 3 The Proposed Solution
### Hardware Realization  
The proposed solution can be easily implemented for any 3D NAND Flash memory architecture. It makes use of two \(k\)-bit ALFSRs, where \(k=\lceil\log_{2}N_{P}\rceil\) (i.e., \(k=8\) for \(N_{P}=256\); \(k=9\) for \(257<N_{P}\leq 512\), and so on). As shown in Figure 7, one ALFSR is used to generate the Seeds for the second ALFSR, which is used to generate the sequence randomizing the data in a page.  
The algorithm to be applied is the following:  
1. After a block erase operation, initialize ALFSR\({}_{1}\) with \(SEED_{IN}\). \(SEED_{IN}\) can always be the same or, more conveniently to avoid a reliability degradation of 3D NAND Flash cells during endurance stress, can be generated randomly and saved in a memory location. It is mandatory to retrieve the selected \(SEED_{IN}\) since it must be used to reconstruct the data sequence during a read operation;
2. Download the content of ALFSR\({}_{1}\) into ALFSR\({}_{2}\). In practice, the Seed of ALFSR\({}_{2}\) is the present state of ALFSR\({}_{1}\);  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
0  
3. Program the memory page by cycling ALFSR\({}_{2}\) until the completion of the page programming while keeping ALFSR\({}_{1}\) on hold, preventing an evolution of its internal state;
4. Send a clock pulse to ALFSR\({}_{1}\) that moves to the next state;
5. Repeat steps 2 to 4 until the completion of the block programming.  
By considering a \(N_{p}\) = 256 \(\times\)\(N_{B}\) = 217 memory block and an 8-bit ALFSR, the data provided by the proposed method consist of a sequence of 514 pseudo symmetrical arrays, each with 256 rows and 255 columns, as shown in Figure 8. In any array, the statistical properties provided by the 8-bit ALFSR are guaranteed. Since the ALFSR period is \(2^{k}-1\), we can avoid issues related to the logical period that are powers of two.  
A \(k\)-bit counter is required if the memory block is not programmed sequentially page after page. The counter is preset with the index of the page to be programmed. ALFSR\({}_{1}\) is initialized with \(SEED_{IN}\) as in point 1 of the described algorithm. Then a countdown starts and, with every clock pulse, ALFSR\({}_{1}\) moves to the next state. When the counter reaches the zero state, the content of ALFSR\({}_{1}\) is downloaded to ALFSR\({}_{2}\) (as in point 2 of the algorithm) since it represents the correct Seed for the page to be programmed.  
When the memory block is read sequentially page after page, the same procedure used for data programming is applied. When a single page is to be read, the procedure used for non-sequential programming is applied with the counter initialized with the page address.  
Data stored in the memory array can be easily reconstructed by XORing the data saved in the memory cells with the ALFSR\({}_{2}\) content, depending on the randomizing scheme (see Figure 9).
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 1 Introduction
However, it did not assess the detailed impact of these strategies on noise sources, propose measures to mitigate the feedback path's noise, or consider misalignments in the suspension system, such as between the displacement transducer, force transducer, and center of mass [23]. In general, optimizing instrument output noise can be approached from two perspectives (as shown in Figure 2): reducing noise at the source or minimizing noise transmission along the propagation path. The current research on noise suppression in VBB seismometers, however, presents two notable gaps: (1) a predominant focus on the noise source, with limited attention to noise transmission paths; (2) from the perspective of noise transmission paths, prior studies have only briefly touched on suppressing forward-path noise without a mention of the degree of impact on noise, the affected frequency band range, and the key feature points, while noise in the feedback path remains underexplored. To address these issues, this paper provides a comprehensive study of all noise transmission pathways within the VBB seismometer. We develop a detailed noise transmission model, examining how pathways shape the noise spectrum and identifying key characteristics that influence the noise transfer function. This level of thorough, systematic analysis of noise propagation has not been conducted previously, and it lays a solid theoretical foundation for future work aimed at optimizing seismometer noise in a specific frequency range through improvements in transmission paths. For example, VBB seismometers provide both velocity and  
Figure 1: Median self-noise models for some typical VBB seismometers [19]. mass displacement outputs, allowing for the potential reduction in self-noise by modifying the output point without altering the noise source itself. In this paper, Section 2 presents a theoretical model of a VBB seismometer that encompasses both system components and noise sources, along with a general noise source model. In Section 3, we analyze the transfer function of five noise sources to equivalent input noise (EIN), examining their spectral characteristics to identify key influences. Based on these insights, we propose a self-noise model. In Section 4, we use the CS60 seismometer for numerical simulations, and the results align closely with theoretical analysis, validating our models. In Section 5, we conduct self-noise testing experiments, comparing measured results with theoretical expectations, with findings showing a high degree of consistency. In Section 6, we provide a discussion of and conclusion to this paper.
# An Improved GreenOFDM Scheme for PAPR Reduction
## 1 Introduction
These further complexity reductions are based on a strategy (IFFTs-on-demand and and hierarchical sampling methods) proposed in [28] to reduce the implementation complexity of the original GreenOFDM, and are shown to be achievable with great efficiency for the proposed technique. This document is organized as follows: First, a brief OFDM and PAPR description is given. Then, SLM-OFDM and GreenOFDM algorithms are succinctly presented before introducing the proposed method. In the last part, computer simulations of the PAPR distribution are presented to clearly demonstrate the improvement of the proposed technique and finally conclusions are drawn.
## 3 Materials and Methods  
### Optical Remote Sensing: MODIS  
The evolution of the snow cover on the glaciated and not-glaciated areas has been described using high-time resolved optical data based on the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard on the Terra & Aqua platforms. While the M.D10A.006 product can provide the Terra/Aqua Snow Cover Daily Global 500 m [34; 35], the M.D09 GA.006 can support the estimation of the Terra/Aqua Surface Reflectance Daily Global 1 km and 500 m [36; 37]. The first product is characterized by the information concerning the cloud cover for each pixel, useful for performing cloud masking, and the estimation of the Normalized Difference Snow Index (NDSI), suitable for discriminating snow-covered pixels from other types of surfaces (especially soil and rocks). Since the two platforms provide two overpass per day, it is possible to have a daily revisit of the study site even if the spatial resolution is coarser than other platforms. The second product provides the daily spectral reflectance, atmospherically corrected, for 7 bands ranging from 400 nm to 2150 nm. We considered for each pixel a time-series of 246 layers (two layers per day) having 123 days every melting season: from the Julian day 122 (1 st May) to 246 (31 st August). All of the cloud-covered pixels can be discarded and a daily check between the two available platforms can confirm the final estimation both in terms of NDSI and spectral reflectance at 500 m resolution. The analysis has been focused on characterizing firstly the different melting seasons, from 2016 to 2018, looking at the Snow Cover Area (SCA) on bare soil/rock areas. Similarly the analysis has been concerned on estimating the occurrence of snow cover on glaciated areas, estimating the first day without snow, the \"First Snow-Free Day\" (FSFD) and the lowest reflectance value at 660 nm (band 1). The first discrimination, between snow/firn and ice surfaces, has been approached following the threshold proposed by [PERSON] et al. [13], based on reflectance at 860 nm (band 2) below 0.6. The estimation of the lowest value at 660 nm has been based on defining for each pixel the time window where snow cannot be detected and calculating the reflectance median in that time interval. The importance of \(\lambda_{660}\) derives from [PERSON] et al. [13] that proposed a threshold of 0.4 for discriminating between \"clean\" ice and \"dirty\" ice.  
Figure 1: Overview of the study area of 79 NG, Greenland. Background is a Sentinel-2 image from 28 July 2017. The red line denotes the equilibrium line altitude (ELA) taken from RACMO2 surface mass balance, the blue dotted line the simulated line of ice density at pore-close off. The orange boxes are the region of interest used in Figure 7 and the tent symbol near region 1 marks the location of the study of [PERSON] et al. [31].
## 7 Performance Evaluation and Results
### Results and Discussion
#### 7.3.1 Complex Segment 1  
The proposed model achieved the highest results where the \(R^{2}\) and adjusted \(R^{2}\) achieved over 80 percent of accuracy in terms of predicting highway traffic flow for Segment C of CS1. We observed that traffic flow patterns in this complex segment become more abnormal for the segments that are close to the exit. Figure 6 shows the DMLRF predicted delay time compared with actual delay time for all segments of CS1. The results for Segment C of Complex Segment 1 were compared with those of other baseline regression models in terms of predicting the weekly traffic flow across all complex segments, as shown in Table 3. The proposed model outperform the other models in all the segments. The proposed model achieved 85% in terms of \(R^{2}\) and \(AR^{2}\) for Segment C, which is the highest accuracy. Moreover, the proposed model achieved the lowest error in terms of MSE, RMSE, and MAE, which is presented in Segment B. Figure 7 compared the performance of the DMLRF with actual data of traffic flow for all segments in CS1. Figure 8 presents the performance of DMLRF model in terms of predicting a week of traffic flow for all the segments of CS1 and is compared with the actual SRI value. If the SRI value of predicted average speed is 4 (the threshold value) for each detector of CS1, the total travel time will be around 4 min. So the total delay is 2 min. However, if the SRI value reaches 9 to 10, the total travel time will be from 24 min to 2 h. So an early warning message should be provided for all approaching vehicles before entering this area. For this complex segment, the highest SRI value was 4 and occurred in Segment C. Segment C is located at the beginning of a curved road and is close to an exit, which increases the level of congestion in this area of I-95 highway. Next, analysis and discussion will be provided for adjacent complex segments.  
Figure 6: DMLRF predicted delay compared to actual delay for Segments A, B, C, and D of complex Segment 1 (tested on one week with more abnormal traffic flow cases).