esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The operational state of a rotor system directly affects its working efficiency, and the axis trajectory can accurately characterize this state. Therefore, a method for extracting axis motion trajectory characteristics based on distance sequence representation is established. First, the axis trajectory sample signal is constructed from the original vibration displacement signal. Singular value decomposition (SVD) is performed on the sample signal to obtain effective components, resulting in a purified and denoised axis motion trajectory signal. Next, the axis motion trajectory signal is centralized and normalized. Feature extraction is then performed on the axis motion trajectory signal. Based on the different curvatures of various regions in the axis motion trajectory graph, data points are adaptively selected. The distances between the selected data points and a unique fixed point are calculated in the two-dimensional plane, resulting in a feature signal that characterizes the axis motion trajectory graph. This completes the extraction of the axis motion trajectory characteristics. Different rotational speeds, additional weights, and changes in rotor arrangement types are applied to a multi-disk rotor test rig to obtain measured data for various unbalanced states, validating this method. The results show that this method effectively characterizes the axis motion trajectory with strong generality.  
axis trajectory; feature extraction; rotor system; unbalanced state +
Footnote †: journal: Applied sciences
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Adversarial Model  
A key exchange protocol without a well-defined adversarial model cannot be considered secure. Hence, here we summarize the assumptions regarding potential adversaries. We assume an active network adversary, who may take full control of all the communication channels (namely, the communication channel between the Guardian and the Child as well as the one between the Guardian and the Parent). This attacker can eavesdrop on messages, modify them at will, inject its own message, delete messages, delay message delivery, duplicate any message and/or replay it later. It may even be able to initiate new protocol sessions and interleave messages from different sessions [43; 44].  
To facilitate the dynamic establishment of the mutual trust between the Guardian and the Parent, we assume a trusted certification authority, e.g., a Public Key Infrastructure (PKI), is available so thatthe more capable parties (i.e., the Guardian and the Parent in our context) can use their long-term private information, such as private keys and certificates, to authenticate their identities with the assistance of the trusted certification authority. With respect to the Guardian and the Parent, if its long-term private information that is used to authenticate itself is leaked and is accessible to attackers, then we consider it to be corrupted.  
As for the Child, we assume it is a resource-constrained device with necessary hardware protections. That means the adversary is at most a weak software adversary but it is not a hardware adversary. The attacker can only access and/or overwrite regular applications or the operating system, but not non-volatile secure storage, such as a Trusted Execution Environment (TEE) [39; 40]. Also, we assume the adversary cannot modify the hardware circuitry. With that being said, the \"Parent\" key (shown in Figure 8) representing the pre-existing trust relationship between the Parent and the Child should be protected properly, e.g., in a TEE or implemented using separate hardware circuitry. If the \"Parent\" key on the Child is leaked or modified, we consider the Child to be compromised.
# A Review of Early Injection Strategy in Premixed Combustion Engines
## 1 Introduction
### Alternative Fuel  
Changing the fuel properties and using alternative fuel are also promising ways to improve the combustion and emissions of HCCI and PCCI engines [27; 28; 29]. Biodiesel fuel, as one alternative diesel fuel, is currently of great interest and an important research subject. Biodiesel fuels contain oxygen and thus provide an effective way to eliminate the over-rich regions and enhance the combustion process, resulting in low soot, HC, and CO emissions [30; 31; 32; 33]. Dimethyl ether (DME) is another alternative fuel. Its good ignition capability and high latent heat lead to decreased cylinder temperature in the combustion phase [34; 35]. Besides, the oxygenated molecular structure and good atomization properties help in the formation of a leaner and more homogeneous mixture. The alternative fuels bioethanol and n-butanol are also widely used due to their high oxygen concentration [36; 37; 38; 39; 40].  
As HCCI combustion is mainly controlled by chemical kinetics, the combustion process and burning rate are dependent on fuel properties. Studies have shown that optimal physicochemical properties are needed under different operating conditions; e.g., fuel with a high cetane number is required for light loads and high-octane fuel for heavy loads [41; 42; 43; 44]. Gasoline/diesel dual-fuel combustion was proved to be a useful approach to control the combustion phasing and heat release rate of HCCI by adjusting the blending ratio according to different operating conditions [45; 46].  
Though many researchers have used different early injection strategies in order to reduce emissions and improve the performance of HCCI and PCCI engines, there is no comprehensive review on the effects of early injection parameters in the literature. From this point of view, the purpose of this paper is to review the effects of various parameters of early injection, including injection pressure, injection timing, injection angle, and blending ratios of alternative fuel on the performance and emissions of HCCI and PCCI engines.
# A Study of Wind Shear Influences on the Aerodynamic Performances of a UAV Airfoil
## 2 Numerical Simulation Model and Shear Wind Model  
### Numerical Model  
As an important aerodynamic component of UAVs, the aerodynamic distribution of the wing is of great significance to an aircraft. The findings of the current investigation are intended to lay fundamental research for the design and flight attitude control of a small UAV. The drone has been designed with a wingspan of two meters, and a maximum flight speed of 100 m/s. A symmetrical wing airfoil NACA0012 is chosen for the drone.  
In this paper, we study the effect of shear wind on the aerodynamic performances of a two-dimensional NACA0012 airfoil with a chord length of 0.573 m using ANSYS Fluent software. In the numerical calculations, the finite volume method is used to discretize Navier-Stokes equations, a second-order windward format is used for spatial discretization, and a k-\(\omega\) SST turbulence model [24] is selected. A 15-times chord length is used for the far field in the computational domain. A C-H type structure grid with a size of 715 \(\times\) 201 is generated, and the first layer grid height is generated according to the law of \(y+\) = 1. Details of the grid are shown in Figure 1.
# A Review of Smart Grid Anomaly Detection Approaches
## 3 Results and Analysis
### Electrical Data Anomalies  
The treatment of problems in data collection about voltage, current, power quality, frequency, and phase angle are considered. A summary can be found in Table 9.  
Short-duration voltage sags and momentary current surges are studied using pattern recognition techniques to investigate the power signal and diagnose the voltage sag in the power grid using SVM and decision tree (DT). According to these analyses, the decision tree algorithm produces the best solution [23].  
There are multiple approaches on the analysis of data coming from PMUs to detect anomalies in some of the measurements, for example: score-based detector arrays [43], hierarchical temporary memory (HTM) capable of unsupervised learning [54], application of big data tools (MapReduce) [45], random matrix theory, and new statistical models using massive data sets in the power grid [44]. In the case of HTM, a limitation is observed in not being able to classify anomalies based on the cause of the anomaly.  
Machine learning is useful for the detection and classification of anomalous synchrophasor data by analyzing a selected window of data points using a combination of three unsupervised methods: isolation forest, K-means, and local outlier probability (LoOP) [65]. One interesting thing about this framework is that it does not require any training data, and experimentally, it has been observed that it can work with high accuracy in real time.  
For power distribution systems, new AI techniques like hyperdimensional computing (HDC) are being used to find electrical load anomalies so that they do not have to rely on preprocessing and can focus on finding problems in real time [66]. Here, the experimental results showed better results than SVM, KNN, and LSTM, and the HDC approach works on raw data without preprocessing.  
When there is not a lot of labeled data to work with, deep learning models that use federated learning and fog computing (Fed-SCR) get satisfactory results for finding power data that does not seem right [74].  
\begin{table}
\begin{tabular}{c c c} \hline _Scenario_ & _Method/Technique_ & _Objective_ \\ \hline Voltage drops in the electrical network & SVM, Decision Tree (C4.5) & \\ \hline Voltage/power anomalies & \
\begin{tabular}{c} Comparison of values with \\ established ranges, Fed-SCR \\ \end{tabular} & \multirow{2}{*}{Detect anomalies} \\ \hline Anomalies from PMU &
\begin{tabular}{c} Unsupervised learning, HTM, \\ MapReduce, Random matrix, \\ isolation forest, K-Means, LoOP \\ \end{tabular} \\ \hline Electrical load anomalies & Hyperdimensional Computing & \\ \hline \end{tabular}
\end{table}
Table 9: Electrical data anomalies.
# GPU-Enabled Serverless Workflows for Efficient Multimedia Processing
## 5 Results and Discussion
### GPU and CPU Comparison for Video Processing  
Before the experimentation of the whole workflow, the video processing function was evaluated in order to determine the instance type that would best suit the requirements of the application in terms of execution time and cost. To demonstrate that GPU acceleration is worthwhile in deep learning inference processes, a comparison was performed using different EC2 instances to process the same 4-min video with a 1280 \(\times\) 720 resolution, consisting of a total of 6000 frames.  
To test the execution time on CPU, the Batch compute environment was configured to use m5.xlarge instances, which have four virtual CPUs of the Intel Xeon Platinum (Skylake-SP) processor with a clock speed of up to 3.1 GHz, with 16 GB of RAM. The on-demand cost of this instance type is 50.192 per hour. In order to take advantage of the multiple vCPUs, Darknet was compiled with OpenMP support and jobs were configured to simultaneously use all the four vCPUs.  
The performance over GPUs was measured using two different instance types: p2.xlarge, with 1 NVIDIA Tesla K80 GPU, 4 vCPUs, and 61 GB of RAM, which costs $0.9 USD per hour on demand; and g3s.xlarge with 1 NVIDIA Tesla M60 GPU, 4 vCPUs, and 30.5 GB of memory, with an on-demand cost of $0.75 USD per hour. To leverage GPU acceleration, Darknet was compiled with CUDA.  
Figure 6 shows the time and cost of an execution to process the same video using our platform with different EC2 instances. A single execution has been deemed adequate as the results show the considerable advantage of using GPU-based acceleration. As can be seen, the reduced cost per hour of the m5.xlarge instance does not outweigh the long duration spent for the execution. Therefore, it is highly recommended to accelerate via GPU such applications, not only to improve the processing time but also to increase savings.  
Figure 5: Time and cost analysis of the _audio2 srt_ function for different audio durations.  
Among the analysed GPU instances, the best performer was the g3s.xlarge. This is due to the fact that it has a lower end graphics card but of a later generation. This way, even with fewer GPU memory, i.e., 8 GB instead of the 12 GB available in each NVIDIA Tesla K80 GPU, it is able to perform a higher amount of operations at the same time. Furthermore, it has less RAM, which also affects its price. These reasons have led to the choice of the g3s.xlarge instance in the compute environment used by the video processing function of the workflow.
# Practical Considerations in the Design of Passive Free Piles in Sliding Soil
## 5 A Comparison with a Real Case of Reactivated Quiescent Landslide in Stiff Soil  
The data in Figure 11 suggest that the displacements d\({}_{\text{yd}}\) causing yielding for bending are relatively small, not exceeding 30 cm; for these levels of displacement, the pile may attain the design shear strength, V\({}_{\text{ult,d}}\), especially in the mode of deformation compliant with the Model 1.  
A situation of this type has been reported in a case study regarding the stabilisation of the Tusa landslide in Sicily (Italy), in which a layer of altered clay was sliding above  
Figure 12: Concrete pile 1200 mm in diameter and 2% of longitudinal reinforcement: head displacements d\({}_{\text{ult,d}}\) which induce the design ultimate moment M\({}_{\text{ult,d}}\) in the most stressed cross-section.  
the stable formation of scaly clay [28]. Both formations were made up of 70% clay, 20% silt and 10% sand. The laboratory tests provided an average liquidity limit of 55%, an average plastic limit of 20% and an average plasticity index of 35%. The angle of shear strength obtained both from direct shear tests and from triaxial tests was about 18\({}^{\circ}\) and the effective cohesion on average \(c^{\prime}\) = 30 kPa. For the given soil plasticity and clayey fraction, the residual angle of shear strength could have reached a value of about 10\({}^{\circ}\)-12\({}^{\circ}\) during the landslide reactivation.  
For stabilisation purposes, an instrumented drilled pile 1200 mm in diameter, 22 m in length, reinforced with 18 \(\mathcal{O}\) 24 and equipped with an inclinometer tube, was installed. The slip surface was localised at a depth of about 10 m from the surface and the groundwater level was identified well below the slip surface. After 85 days from the pile casting, the inclinometer indicated a displacement of the pile head of approximately 3.5 cm. Between 18 February 1988 and 7 April 1988, the displacement reached 28.5 cm, with an increase in approximately 25 cm over 50 days. In these conditions, the pile showed collapse just near the sliding surface.  
This case study seems to support the results of this study: since the pile and the soil were almost compliant with the numerical Model 1, the pile should have undergone a BY-type of collapse mechanism with the formation of two plastic hinges. Instead, it collapsed in shear near the slip surface with a final displacement of the pile head of 28.5 cm.
# Quantification of the Effective Detectable Period for Concrete Voids of CLP by Lock-In Thermography
## 5 Results and Discussion  
### Analysis of Simulated Thermography and Deciding the EDP  
The LIT defect analysis tool was applied to the thermography data of all cases replicated through simulation to analyze the SNR trend of temperature/amplitude/phase images in the transient state, and the EDP for each case was confirmed. Table 4 summarizes the EDP derived from the thermography data of each case, and Figure 13 is an example showing the temperature/amplitude/phase trend and SNR trend of the sound part and the void parts in Case 3. Figure 13b is the temperature SNR trend of each void, and the 12-151 frames satisfied the EDP condition. Figure 13d is the amplitude SNR trend, and the EDP was 1-12, 36-59, and 89-107 step sections. Figure 13f shows the phase SNR trend, and the EDP was met in the 3-5, 8-18, 57-69, and 106-115 step sections.  
Figure 11: Acquired thermography of the CLP mockup experiment: **(a)** picture image of the PC monitor and **(b)** acquired temperature image by MATLAB S/W (640 \(\times\) 480 pixels) and analysis area in yellow box (480 \(\times\) 440 pixels).  
Figure 12: ROI indexing in an acquired temperature image (480 \(\times\) 440 pixels).  
Figure 13: Analysis of simulated thermography and EDP (Case 3; 2 kW, 0.01 Hz): (**a**) temperature change; (**b**) temperature SNR trend; (**c**) amplitude change trend; (**d**) amplitude SNR trend; (**e**) phase change trend; and (**f**) phase SNR trend.  
Among the EDP common to all cases, the #74 frame for temperature and the #58 step for amplitude and phase were selected as mutual comparative analysis times. Considering the frame rate of each case, 0.01 Hz corresponds to the temperature at 148 s, the phase and amplitude at 116-141-166-191 s, and 0.02 Hz at 74 s and 58-70.5-83-95.5 s.
## 3 Preliminaries
### Network Model  
The network model includes the Trusted Authority (TA), Roadside Units (RSUs) and vehicle (OBU). The notations used in the paper are organized and presented in Table 1.  
* Trusted Authority (TA): The TA can initialize the system and register the RSU and vehicle. In case of malicious activities, only the Trusted Authority (TA) has the ability to reveal the true identity of the vehicle.
* Roadside Units (RSUs): The RSUs are a series of roadside base stations that assist the TA in authentication and share information with the vehicle after negotiating the session key with the vehicle. In addition, the PUF is installed on the RSU.
* Vehicle (OBU): The OBU is deployed in the vehicle. The OBU is responsible for computation and communication with the vehicle and RSUs via wireless channels.
## 2 Materials and Methods
### Load Demand Seasonality  
Load demand seasonality refers to the periodic fluctuations in electricity load over specific timeframes (daily, weekly, monthly, or annually), which are reflected in time-series load data through regular and predictable variations. Load seasonality is heavily influenced by local factors, such as weather patterns, differences between weekdays and weekends, time of day, local customs and practices, holidays, vacations, and various political and social elements. The most notable seasonal patterns in energy consumption include (a) daily seasonality, which arises primarily because energy use during the day surpasses night usage, and consumption differs during typical office hours compared to non-office hours; (b) weekly seasonality, resulting from differing energy use on weekdays versus weekends, with weekdays showing higher consumption; and (c) annual seasonality, largely attributed to changes in climatic conditions, such as the increased use of air conditioners in summer and heaters in winter. However, there may be other seasonal patterns, whether one or many, that, once identified, can improve the understanding of energy consumption and enhance load forecasting.  
Clustering  
K-means clustering is a well-established unsupervised learning algorithm that partitions data of \(n\) observations \(\{\mathbf{x}_{1},\;\mathbf{x}_{2},\ldots,\mathbf{x}_{n}\}\), where each observation \(\mathbf{x}_{j}\), with \(j=1,\ldots,n\), is a d-dimensional real vector, into distinct \(k(\leq n)\) sets of clusters \(S=\{S_{1},\;S_{2},\;\ldots,S_{k}\}\) based on feature similarity. This is performed by minimizing the Euclidean distance between the vectors and the mean or centroid, \(\mathbf{\mu}_{i}\), of points in the cluster \(S_{i}\), such that  
\[\text{argmin}\sum_{i=1}^{k}\sum_{x\in S_{i}}\|\mathbf{x}-\mathbf{\mu}_{i}\|^{2}\quad, \tag{1}\]where \(\mathbf{\mu}_{i}=\frac{1}{|S_{i}|}\underset{x\in S_{i}}{\sum}\mathbf{x},\left|S_{i}\right|\) is the size of \(S_{i}\), and \(\left\|\cdot\right\|\) is the Euclidean norm [39].  
K-means clustering is computationally efficient, especially when working with large datasets like energy demand records that contain hourly data over multiple years. The algorithm's iterative process of assigning points to clusters and recalculating centroids is relatively fast compared to other clustering techniques, making it suitable for real-time or large-scale applications. Indeed, K-means offers simplicity, speed, and scalability, making it ideal for large energy-demand datasets. In the context of energy forecasting, clustering techniques can be highly beneficial for identifying patterns and segmenting historical load data for more accurate training in the forecasting processing. Among various clustering algorithms, K-means is widely used due to its simplicity and efficiency [40,41].
## 2 Materials and Methods
### Optimization of Different Reaction Parameters for the Maximum Pectinase Activity  
The reaction parameters--NaCl concentrations, substrates, substrate concentrations and reaction times, pH values, and temperatures--were investigated in order to determine the optimal conditions for maximal activity of the purified pectinase.  
The activity of the enzyme was investigated at different concentrations of NaCl (0, 30, 50, 70, and 150 g L\({}^{-1}\)). The effect of substrate specificity on pectinase enzyme activity was determined by incubating 0.05 mL of the enzyme with 0.05 mL of different substrates like apple pectin, citrus pectin, xylan, galactose, and polygalacturononic acid. These substrateswere prepared in 50 mM Tris-HCl buffer, pH 9.0, with a concentration of 10 g L\({}^{-1}\). The reaction mixture was incubated at 40 \({}^{\circ}\)C for 30 min and the enzyme activity was assayed. To examine the effect of substrate concentration during enzyme-substrate reaction, varying concentrations of citrus pectin g L\({}^{-1}\) (5, 10, 15, 20, 25, and 30) were prepared and then enzyme activity was measured. For determination of the optimum reaction time, the reaction mixture was incubated for different time intervals (10, 20, 30, 40, 50, and 60 min) under optimized temperature and pH. The pH optimum was determined over a range of pH from 5.0 to 11.0 with the use of different buffer solutions by dissolving the substrate in 0.2 M acetate buffer (for pH 5.0-5.5), phosphate buffer (for pH 6.0-8.0), or glycine-NaOH buffer (for pH 8.0-11.0), and enzyme activity was assayed at 35 \({}^{\circ}\)C. The optimum temperature for the enzyme activity was determined by incubating the reaction mixture at pH 8.5 and different temperatures in the range 30-70 \({}^{\circ}\)C. The stability of the pectinase at the optimum temperature and pH was also investigated at various time intervals. Temperature stability of the pectinase was determined by preincubation of the enzyme in Tris-HCl buffer (pH 8.5) at 40-80 \({}^{\circ}\)C in the absence or presence of a substrate, and the residual activity was determined at 40 \({}^{\circ}\)C. The pH stability of the enzyme was determined by measuring the residual enzyme activity after incubation in the corresponding buffer at 40 \({}^{\circ}\)C for 10-60 min. The enzyme activity without preincubation was denoted as 100%.