esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
## 6 Experimental Results and Discussion
### Result and Discussion  
Table 6 shows the performance of all four models (LR, SVM, DT, and ANN). Figure 11 shows accuracy for each of the four experiments that were conducted on the IoTID20 dataset. These results indicate the feasibility and efficiency of our approach using the four ML algorithms to detect malicious and normal (benign) nodes. The ROC curves of (1) LR, (2) SVM, (3) DT, and (4) ANN are discussed and shown in Figure 12. The ANN model produced the highest detection rate (DR) for three attack types, namely Mirai-ACK flooding (M.A.F), Mirai-HTTP Flooding (M. HTTP.F), and Mirai-UDP Flooding (M.UDP.F). On the other hand, the DT model produced the highest detection rate (DR) for MITM ARP  
Figure 8: Services Distribution.  
Figure 10: The subcategories of attacks in IoTID20 dataset.  
Figure 9: The main categories of attacks are found in the IoTID20 dataset.  
Spoofing (M.ARPS), Mirai-Host brute force (M. Host. B. force), Scan Host-port (S.H. P), and Scan Port OS (S.P. OS).  
The ROC-AUC is a more important metric to represent the performance of a prediction/classification model. The ROC-AUC clearly demonstrates the ability of different ML algorithms to accurately detect various malicious attacks in network traffic. The ROC-AUC score is a measure of the diagnostic ability of the classifier model. ROC is a probability curve, while AUC represents the measure of separability accuracy rate under various threshold tunings and rank features. The higher the percentage of accuracy (i.e., more AUC), the better the model prediction and/or classification performance. One of the main advantages of the ROC is to compare between algorithms as Figure 12 shows. The ROC-AUC curve was generated to outline multiple classes. Therefore, it is preferable to use the ROC-AUC first to understand which algorithms are the most accurate toward determining (i.e., down selecting) the prime ML algorithms before embarking on building a detection/prediction model. Figures 11 and 12, and Table 6 answer our experimental question, therefore, to achieve a high rate of accuracy. In this way, we used various threshold tunings to obtain high accuracy and performance rates. Furthermore, the challenges in cybersecurity issues are related to dealing with unbalanced categories. In particular, IoT ecosystem generates locally in an individual sensor/node or globally in a centralized location enormous data, which results in a general, imbalance dataset. An imbalanced dataset problem prevents achieving high accuracy in shallow machine learning algorithms due to the different weights in each class.
## 2 Methodologies
### Feature Fusion and Representation  
In the task of text classification, both convolutional neural networks (CNNs) and attention mechanisms serve as crucial feature extraction modules, capable of capturing rich local and global features. However, in practical applications, we have observed that fusing local and global features can further enhance the model's performance. Therefore, in this paper, we employ a residual connection to integrate the outputs of the convolutional and attention layers, aiming to strengthen the model's generalization ability. Specifically, we define a residual connection function that adds the outputs of the convolutional and attention layers and feeds them into a fully connected neural network (_FFN_) with two hidden layers, thereby enhancing the model's representational power. The calculation formula for this residual connection function is as follows:  
\[FFN(Concat(O_{1},O_{2},\ldots,O_{l}))+Concat(M_{1},M_{2},\ldots,M_{l}) \tag{7}\]  
In terms of text representation, various methods are commonly employed to transform the input text sequence into a fixed-length vector representation for further processing by the model. Common approaches include taking the maximum or mean value. In the case of the BERT model, a special token \"[CLS]\" is added to the original input sequence, and after training the model, the vector corresponding to \"[CLS]\" is extracted as the representation of the entire text. Maximum representation emphasizes key information and may be more effective for tasks that prioritize important keywords or features. The output vector of the \"[CLS]\" token contains rich semantic information but may not fully consider features from different positions in the text sequence. On the other hand, mean representation focuses more on the fusion of overall features and is suitable for more global tasks. Therefore, in this paper, we choose to perform a mean operation on the word vectors after the residual connection to obtain a vector representation of the entire sentence sequence.  
After the aforementioned steps, we obtain higher-level feature representations of the given text sequence. These feature representations are then passed to subsequent fully connected layers, where a series of linear transformations and activation functions are applied to further extract and combine features. Finally, a softmax layer is used for classification prediction, producing a probability distribution over the categories to which the text belongs. The entire process can be viewed as an end-to-end training process, optimizing network parameters through the backpropagation algorithm to maximize prediction accuracy.
# Quantitative Characterization of _Geotrichum candidum_ Growth in Milk
## 2 Materials and Methods
### Inoculum Preparation and Experimental Design  
G. candidium was cultivated for 72 h on the upper layer of a horizontal solidified SMA agar in a tube at 25 \(\pm\) 0.5 \({}^{\circ}\)C. Parallel cultivated isolates were then mixed with 10 mL of sterile peptone-saline solution and released through soft rubbing of the agar's surface with a sterile cell scraper. Immediately after the preparation, an appropriate decimal dilution of this culture was used to inoculate 300 mL of pre-tempered ultra-high temperature processed (UHT) milk with an initial level, which ranged approximately from 1.5 to 2.0 CFU\(\cdot\)mL\({}^{-1}\).  
The effect of NaCl addition (0 and 1%, \(w/v\)) on the kinetic growth behaviour of _G. candidium_ isolate J was evaluated in UHT milk (1.5% fat content; Rajo Inc., Bratislava, Slovak Republic) at temperatures of 6, 8, 12, 15, 18, 21, 25, 30, 34, and 37 \(\pm\) 0.5 \({}^{\circ}\)C under static aerobic conditions. The aforementioned temperatures were selected in an attempt to fully cover the growth region of the species to the greatest possible extent. Considering the salt sensitivity of _G. candidium_ contrary to the majority of other dairy yeasts, only addition of 1% NaCl was evaluated. To obtain well-fitting data, three replicates were performed per experiment.  
Actual viable counts of the isolate were determined at predefined time intervals based on the incubation temperature by plating on DRBC agar (Biokar Diagnostics, Beauvais, France) according to EN ISO 21527-1 [29] with aerobic incubation at 25 \(\pm\) 0.5 \({}^{\circ}\)C for 5 days. The pH of the milk samples was analysed using a calibrated WTW 720 pH-meter (Inolab, Weilheim, Germany) equipped with a Sen Tix 81 glass electrode (WTW GmbH, Weilheim, Germany) with the same time interval as the microbiological determination.
## 5 Experimental Verification
### Influence of Power Grid Strength on the Stability of GCI Systems  
GCI grid-connected currents controlled by two different domains under various power grid intensities are depicted in Figure 11 with voltage feedforward control considered.  
As shown in Figure 11a, with an inductance \(\mathrm{Lg}=1\) mH, the GCI system controlled by PI in the \(dq\) domain is stable. When \(\mathrm{Lg}=5\) mH, the grid-connected current exhibits waveform distortion and harmonic oscillation, indicating poor system stability. When  
Figure 11: Grid-connected current waveform under different power grid strengths, considering voltage feedforward. (**a**) The dq-domain PI-controlled GCI system; (**b**) the phase-domain PR-controlled GCI system.  
Figure 10: RTLAB experimental platform.  
Lg = 16 mH, the grid-connected current diverges significantly, leading to serious instability. Figure 11b shows that with Lg = 1 mH and 5 mH, the GCI system controlled by phase-domain PR is stable; when Lg = 16 mH, it also shows waveform distortion and harmonic oscillation, reflecting poor system stability.  
GCI grid-connected currents controlled by two different domains under varying power grid intensities are shown in Figure 12 with voltage feedforward control not considered.  
It can be observed from Figure 12 that when the grid inductance \(L_{\text{g}}\) is 1 mH, 5 mH, and 16 mH, the GCI systems controlled by PI in the \(dq\) domain and PR in the phase domain are both stable.
## 2 Materials and Methods
### CMR Acquisition Protocol  
CMR acquisition followed a standardized protocol established at our center. A standard 1.5 Tesla CMR scanner (MAGNETOM Avanto(r), Siemens Healthineers, Erlangen, Germany) was employed using a 12-element cardiac phased array coil. For volumetric assessment, we obtained axial, multiphase, steady-state-free-precession (SSFP) cine images using retrospective ECG-gating. Axial slices were preferred over conventional short-axis slices due to their superior reproducibility and were obtained using coronal and sagittal localizers [14,15]. To minimize motion artifacts, breath holding was performed during expiration. Slice thickness was 4 to 8 mm depending on the patient's body weight. We acquired one slice per breath-hold (8-12 s), capturing 25 phases per cardiac cycle for each slice. An acquisition matrix of 192 \(\times\) 192 was used.  
For the purpose of volumetric assessment, the endocardial contours of the functional RV and left ventricle (LV) were manually outlined in axial slices at both end-systole and end-diastole. Papillary muscles and trabeculations were excluded from these tracings as they are considered part of the cardiac mass. The atrialized RV was excluded from the measurements, as it does not contribute to right ventricular ejection fraction (RVEF) [15; 16].  
Measurement of aortic flow, pulmonary artery flow, and flow through the TV was conducted using two-dimensional phase contrast images. The regurgitant volume and fraction of the TV were obtained from phase contrast images en face to the tricuspid valve orifice. In cases where this measurement was not technically feasible, the regurgitant volume and fraction were calculated from the pulmonary artery flow and the right ventricular stroke volume as described by [PERSON] et al. [16].  
CMR-FT analysis was executed utilizing short-axis and four-chamber SSFP cine images. Subsequently, all CMR data were transferred to a dedicated workstation for post-processing (Circle Cardiovascular Imaging Inc., CVI42(r) 5.12.1 software, Calgary, AB, Canada).
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 4 Hybrid Sensor Fusion Algorithm Overview
### Testing Obstacle Detection and Tracking using Kalman Filtering
#### 4.2.2 LiDAR Point Cloud Data Processing  
LiDAR gives us rich information with point clouds (which include position coordinates x, y, z and intensity i) as well as a depth map. The first step to process the LiDAR point cloud data (PCD) as shown in Figure 11 involves downsampling and filtering the data. The raw LiDAR point cloud is high resolution and covers a long distance (for example a 64-lens laser acquires more than 1 million points per second). Dealing with such a large number of points is very computationally expensive and will affect the real-time performance of our pipeline. Therefore, we downsample and filter. We downsample using a voxel grid; we define a cubic voxel within the PCD and only assign one point cloud per voxel. After downsampling, we use a region of interest (ROI) box to filter any PCD outside of that box that is not of our interest (i.e., points from non-road objects like buildings). In the next step we segment  
Figure 10: 1D and 2D-CFAR sample windows (left) with 1D-CFAR multi object detection (right). The plot shows signal strength vs. range. Five objects are detected (the blue peaks) at ranges 50, 100, 200, 350 and 700 feet.  
the filtered PCD into obstacles and the road. Knowledge of the road and objects on the road are the two segments most important for the automated driving task. The PCD segmentation happens at the point level; hence, processing would require a lot of resources and slow down the pipeline. In order to improve the performance of our pipeline, similar to the radar data, we cluster the obstacle segments based on their proximity to neighboring points (Euclidean Clustering) and assign each cluster with a new position coordinates (x, y, z) which is the mean of all the point clouds within that cluster. Finally, we can define bounding boxes of the size of clusters and visualize the obstacles with the bounding boxes.  
For segmenting the PCD into road and obstacles, we need to separate the road plane from the obstacle plane. In order find the road plane, we use the Random Sample Consensus (RANSAC) method [39]. Using RANSAC, we fit a plane to our 3D point cloud data. At each step, RANSAC picks a sample of our point clouds and fits a plane through it. We consider the road points as inliers and obstacle points as outliers. It measures the inliers (points belonging to the road plane) and returns the plane i.e., road plane with the highest number of inliers or the lowest number of outliers (obstacles).
## 1 Introduction
### Diagnostics and Prognostics Using Hidden Markov Model (HMM)
A range of hyperparameters is then set to train the model with a truncated approximation for HDP. The model then estimates the optimal number of hidden states for the given oil degradation trajectory. The number of states will vary according to the observed trajectory. The wear debris concentration (WDC) in lubricating oil provides a vital degradation feature that indicates the lube oil and system health status. Here, we simulate realistic traces of the degradation signal based on WDC by utilizing the wear model proposedby [PERSON] et al. [34]. The relation of wear rate with wear debris concentration is defined using a system-based model in their work. The authors [34] simulated WDC for the severe wear rate phase of the operating system. The severe wear rate (wear-out) phase of the operating system generates more wear debris and will require more oil replenishments and oil changes to avoid breakdown. The other two phases in a typical lifecycle bathtub curve, i.e., burn-in, and steady-state wear rates see fewer oil replenishments and oil changes, generating an almost linear or monotonic oil degradation trajectory with random noise. The previous work of several researchers [26; 32; 33; 35] has already proven the efficacy of HDP-HMM with different trajectories. The focus of our work is to check the ability of the HDP-HMM model to identify hidden states of lubricating oil degradation with irregular replenishments and oil changes. The criticality of the operating system and lubricating oil degradation in the wear-out phase motivated this study. This study also introduces degradation drops due to external interference events, namely, oil replenishment and oil change, to the simulated data. The effect of these events is evident in the experimental work by [PERSON] et al. [2]. In addition to oil replenishment and oil change, lubricating oil degradation can be influenced by contamination, operating conditions (air, moisture, water, gas, radiation, etc. ), storage conditions, operating temperature and pressure, speed of tribological component, oil selection, change in chemical properties, etc. The proposed method considers lubricating oil contamination, i.e., wear debris, to assess the lubricating oil remaining useful life, and it can be utilized for any system where contamination is continuously monitored, and lubricant is under regular replenishments. Based on the above discussions and context of our work here, the subsequent sections address the following aspects. Section 2 presents the simulation methodology for data generation, along with the theory and application of the Dirichlet process (DRP), HMM and, sticky HDP-HMM in the context of the oil wear data. In Section 3, the remaining useful life (RUL) to oil replacement is predicted using the conditional residual function (CRF) and mean residual life (MRL). Finally, Section 4 summarizes the study and its outcomes and presents some ideas for future work leveraging our proposed framework.
## 4 Experimental Results and Discussions
### Measurement of the Focal Length and Beam Size  
The mirror reflection method and the knife-edge method [14] were used to measure the focal length of the fabricated lensed optical fiber and the beam size at the focal plane, respectively.  
The dimensions of the lensed fiber used for these measurements were L\({}_{\text{C}}\) = 308 \(\upmu\)m and R = 72.8 \(\upmu\)m in Figure 5b. Here, we note that the L\({}_{\text{C}}\) value immediately after cleaving with the femtosecond laser was measured to be 300.4 \(\upmu\)m. However, after lens formation with the CO\({}_{2}\) laser, the L\({}_{\text{C}}\) value increased to 308 \(\upmu\)m, with a length increase of \(\sim\)7 to 8 \(\upmu\)m. Figure 7a shows the measurement results of the focal length of the fabricated lensed optical fiber. The intensity of the beam reflected by the
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation
### Individual Code Sequences for Traditional 5G Modulation Subscribers  
Code sequences with a high number of zero elements and a low level of cross-correlation between the signals of various subscribers are frequently used for this method of group signal formation [21]. The building blocks of these sequences can be derived from the 3 GPP-defined BPSK, QPSK, or QAM signal structure standards [22]. Figure 4 shows the transmitting side structure of a NOMA system using the 5G modulation that has been in use up to this point. One of the processing bits can be performed at the higher level bits after noise-resistant coding of the input data stream has been carried out. In the final step, traditional 5G modulation is used to process the data at the character level. The production of group signals using this method is characterized by a low level of mutual correlation between the signals of various subscribers and by the employment of code sequences with a large number of zero elements [23]. Bit-level processing can begin with noise-resistant coding of the incoming data stream or any of the aforementioned techniques. Once the modulation is complete, symbol level processing in 5G legacy mode is executed.  
For the 3 GPP standardization, different organizations have suggested distinct NOMA code sequence architectures for character processing [24]. The Welch and Grassmannian sequences underlie the Ericsson (WSMA), and Nokia (NOCA) schemes, respectively, whereas the generalized Belch sequences method underlie the NTT DOCOMO (UGMA) system. Qualcomm's RSMA scheme, which implements a character-level scrambling technique, is frequently cited as the system used in publications.  
Figure 4: A schematic of a 5G-modulated NOMA transmission system.
## 4 Conclusions  
The specific heat of the carbonate salt mixture-based graphite nanofluids was measured within a wide temperature range of 150-560 \({}^{\circ}\)C. The effects of the chemical compositions of the base fluid, the nanoparticle concentration, and the dispersion homogeneity on the specific heat were investigated. The knowledge gained in this study is summarized as follows.  
1. The specific heat was anomalously enhanced by up to >30% in the liquid phase and 36% in the solid phase via the dispersion of graphite nanoparticles. The enhancements were strongly dependent on the chemical composition of the base fluid: the increments, as well as the enhancements in the specific heat, increased when the chemical composition of the base fluid was far from the eutectic composition.
2. The specific heat increased with the concentration of the nanoparticles. A noticeable increase was observed at the nanoparticle concentration of 0.025 wt % for the base fluid including 75 mol %  
Figure 9: SEM images of the nanofluid (1 wt % nanoparticle and 5 wt % GA): nanoparticle-induced compressed liquid layer structures in solid phase.  
lithium carbonate and 25 mol % potassium carbonate. A critical concentration of nanoparticles was observed. For nanoparticle concentrations exceeding 0.1 wt %, the increment was negligible.
3. The specific heat enhancements increased as the amount of the surfactant (GA) increased from 1 to 5 wt %. Larger enhancements were measured for the nanofluids with 5 wt % GA, and the actual specific heat value was almost double that of the eutectic.
4. The homogeneous dispersion of the graphite nanoparticles was visually confirmed via SEM (secondary electron images and backscatter images) for the nanofluid after a DSC test. In the SEM images, the graphite nanoparticles were spherical, having smooth curvature (not disks), so it was presumed that special structures, nanoparticle-induced compressed liquid layer, were formed in the nanofluids. The SEM images conclusively proved that the dispersion homogeneity (quality) of the nanoparticles is due to the specific heat enhancements of the nanofluids.  
**Author Contributions:** [PERSON] analyzed the data; [PERSON] performed the experiments, analyzed the data and wrote the paper. All authors have read and approved the final version of the paper.  
**Acknowledgments:** This work was supported partly by the New Faculty Research Fund of Ajou University and partly by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018 RIA2B2001082).  
**Conflicts of Interest:** The authors declare no conflict of interest.