esa-sceva/satcom-chunk-collection huggingface dataset:
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 3 Methodology
### Network Design and Operational Challenges  
During the model implementation and analysis, the network design and its operational challenges were mentioned in the system implementation.  
* **Bandwidth constraints:** The transmission of weather data in a vehicular ad hoc network (VANET) can cause bandwidth constraints, particularly during heavy traffic.
* **Security challenges:** Given that the weather data are sensitive, there is a high risk of data interception or theft. Implementing security measures will be crucial in guaranteeing that the data remains secure.
* **Scalability:** The implementation of blockchain technology in a VANET context can be challenging, particularly when it comes to scalability. The network may experience issues if there is an increase in the number of nodes or if the network traffic increases.
* **Reliability:** The reliability of the blockchain-based secure weather forecasting system will depend on the underlying routing protocol in VANETs. If the routing protocol is inefficient, this can result in packet loss or delay, impacting the system's overall reliability.
* **Privacy concerns:** Blockchain technology can raise privacy concerns, particularly if the network is open to third-party nodes. The risk of exposing personal data and implementing privacy measures is crucial.
* **Maintenance:** Implementing the BBSF system will require regular maintenance to ensure the network is functioning correctly. This includes monitoring the system for any potential issues and ensuring that updates are applied promptly.
* **Interoperability:** The BBSF system must be interoperable with other VANET systems to ensure it can communicate with other vehicles on the road. This may require the use of common communication standards or protocols.
## 2 Materials and Methods
### Feature Reduction and Main Clustering
#### 2.2.2 Laplacian Score  
After autoencoding, the complete set of input light spectra represented by their \(1\times 15\) dimensional feature vectors is post-processed by calculating the corresponding Laplacian score [40; 49]. This allows for a further reduction of the dimensionality by keeping only the most important features. The Laplacian score algorithm evaluates the importance of a feature by means of its power of locality preserving. In order to model the local geometric structure of the data, a \(k\)-nearest neighbor graph is constructed, where the weight assigned to each edge of connected data points describes their similarity in terms of an Euclidean distance metric. Making use of locality preserving projections [50], the weight matrix is used to calculate the Laplacian score of the features based on Laplacian eigenmaps [51]. Features with smaller score values are in principle more important than features with higher values. In this work, the score median has been calculated and all features with a larger value have been discarded. The feature vectors representing the input light spectra can thus be reduced to \(1\times 8\) dimensions.
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 2 Materials and Methods
### Experimental Data Acquisition
#### 2.3.2 Data Processing  
The collected data were processed through a three-step procedure:  
* Step 1 (merging variables and surveyed data): The data required for the experiment were constructed by combining the scenarios provided in the interview with the interviewers' response results. The provided scenario is used as a predictor, and the response result is used as an outcome variable.
* Step 2 (variable construction): The relative orientation of the OS to the TS, aspect to the TS to the OS, and speed ratio were selected as explanatory variables. Moreover, the result variable was labeled as a categorical variable. The response result was labeled 0 for head-on, 1 for give-away, and 2 for stand-on. Finally, the data that responded as a safe situation were not used to build the model.
* Step 3 (data pre-processing): Processed data are pre-processed to suit model construction. It is crucial because pre-processing affects the model's performance [29]. Each data point used in this study was divided into training, validation, and test data. First, the entire data are divided into training-validation data and test data. After that, the data are again divided into training and validation data. The division ratio is 7:3. Subsequently, the predictors were standardized. Standardization was performed using only the training data. At this time, the validation data and test data did not affect standardization.
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 5 NAD+ Biosynthesis
### The Nucleoside Pathway  
Nicotinamide riboside (NR) and nicotinic acid riboside (NAR) are other NAD+ precursors that are acquired from the diet. Once they enter the body, the enzyme nicotinamide ribose kinase (NRK) is responsible for phosphorylating NAR, giving rise to NAMN (an intermediate in the Preiss-Handler pathway) and converting NR directly to NMN, bypassing the limiting reaction catalysed by NAMPT of the salvage route [7]. Until recently, it was thought that the cell membrane was impermeable to extracellular NMN generated by extracellular NAMPT and CD38. Thus, NMN must first be dephosphorylated to NR by the enzyme CD73, and then NR enters the cell through nucleoside transporters (ENTS) to equilibrate the pools [3]. However, [PERSON] et al. recently identified an NMN transporter in the cell membrane called Slc12a8 [44; 45]. However, the expression of Slc12a8 is limited to the intestine and others have questioned its ability to transport NMN [46].
## 4 Instrument Design  
### Fundamental Principle  
The basic detection principle of the HEPD is as follows. Both high-energy proton and electron probe systems use silicon semiconductor detectors. When high-energy particles are injected into the sensor through the collimator, they deposit energy in each semiconductor detector and generate corresponding electron-hole pairs in an ionization mode. Under the  
Figure 1: Diagram of high-energy particle detector system composition.  
Figure 2: Photos of the high-energy particle detector ((**Left panel**): \(-\)X probe; (**central panel**): \(+\)Y/Z probe; (**right panel**): electronic box).  
action of a high-voltage electric field, these electron-hole pairs are assembled to the output terminal and generate charge pulses. The charge pulse height is proportional to the energy deposited by the particles in the semiconductor detector. According to the pulse height of the semiconductor detector, the signals are subjected to discrimination threshold analysis (which means analyzing the charge pulse height through electronics) and coincidence or anti-coincidence processing (which means logical working modes of high-energy electron or proton), thereby obtaining the information of the particle spectrum [25; 26; 27; 28]. Figure 3 shows the schematic diagram of telescope system in the HEPD.  
Ion-implanted silicon semiconductor sensors of different numbers and thicknesses are used in high-energy proton and electron detection sensor systems. The function is to measure the energy loss of particles in the sensor and convert the energy into electrical signals for subsequent analysis of electronic circuits. The energy spectrum and flux of high-energy protons and electrons can be measured through the signals of the sensor and by combining with the amplitude analysis method and the appropriate logical working mode.
# Development of Rockburst Research: A Comprehensive Review
## 6 Rockburst Prevention and Control  
Due to the seriousness of rockburst accidents, prevention and control methods of rockburst have always been the research focus of engineers and researchers. It is undoubtedly the most direct means to explore and summarize the prevention and control methods of rockburst from project sites. [PERSON] et al. summarized the experience and lessons of the Coeur d'Aleme district on rockburst accidents in the past 60 years, focusing on the practical measures successfully taken to reduce hazards [169]. [PERSON] et al. summarized the rockburst prevention measures of the Jinping II hydropower station, which effectively controlled rockburst via reducing surrounding rock stress and weakening excavation disturbance [170]. [PERSON] et al. deemed that the combined dynamic and static load is the main cause of rockburst in an irregular working face. Based on this, a cooperative control technology was proposed to weaken the dynamic load through hard roof directional hydraulic fracture and enhance the surrounding rock by a supporting system (See Figure 9) [91]. [PERSON] presented some methods used in Canada to reduce risk of rockburst in deep mines and proposed that with a continuous increase in mine depth, a pervasive installation of a support system with dynamic load bearing capacity may become necessary [171]. [PERSON] et al. summarized pretreatment methods for rockburst, mainly including blasting, decompression drilling and hydraulic fracturing, and introduced the experience of rockburst support in Australia, Canada and South Africa [172]. Figure 8: Rockburst charts: (**a**) one-dimensional rockburst chart: the rockburst intensity diagram represented by \(\sigma_{c}/\sigma_{t}\) (from [93]); (**b**) two-dimensional rockburst chart: performance of the TRC chart and the empirical formulas used to determine the rockburst intensity as compared to the observed values (from [18]); (**c**) diagrammatic explanation of limit values for prediction of rockburst and squeezing from different aspects (from [166]). The main prevention and control measures for rockburst in some mines and tunnels are listed in Table 7 and summarized in Figure 10.  
There is no doubt that support of excavation has always been the key research object of scholars and engineers in rockburst prevention measures. In 1996, the Rock Mechanics Research Center of Laurentian University conducted a five-year support design study for rockburst control. Based on this work, the Canadian Rockburst Support Handbook was published to summarize the characteristics and causes of rockburst failure and provide design methods of the rockburst support system [8]. [PERSON] et al. put forward seven simple principles on rockburst support according to field experience, shown in Figure 11, for a design guide of rockburst support [30; 173]. Figure 10: Control measures of rockburst. Figure 9: Cooperative control technology of rockburst in irregular working face (from [91]).
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
###### Abstract  
Although in-orbit anomaly detection is extremely important to ensure spacecraft safety, the complex spatial-temporal correlation and sparsity of anomalies in the data pose significant challenges. This study proposes the new multi-task learning-based time series anomaly detection (MTAD) method, which captures the spatial-temporal correlation of the data to learn the generalized normal patterns and hence facilitates anomaly detection. First, four proxy tasks are implemented for feature extraction through joint learning: (1) Long short-term memory-based data prediction; (2) autoencoder-based latent representation learning and data reconstruction; (3) variational autoencoder-based latent representation learning and data reconstruction; and (4) joint latent representation-based data prediction. Proxy Tasks 1 and 4 capture the temporal correlation of the data by fusing the latent space, whereas Tasks 2 and 3 fully capture the spatial correlation of the data. The isolation forest algorithm then detects anomalies from the extracted features. Application to a real spacecraft dataset reveals the superiority of our method over existing techniques, and further ablation testing for each task proves the effectiveness of fusing multiple tasks. The proposed MTAD method demonstrates promising potential for effective in-orbit anomaly detection for spacecraft.  
spacecraft; unsupervised anomaly detection; time series; deep learning; multi-task learning; temporal convolutional network; LSTM; VAE +
Footnote †: journal: Applied sciences
## 1 Introduction
### Basics of the Smart City
#### 1.1.2 Smart Education  
Distance learning is a common method of teaching in the current education system [28; 29]. Moreover, future distance learning, or education systems, require a higher quality of Internet service, along with real-time interactions and lower power consumption [29]. Moreover, the improvement of the quality of remote learning and education will enhance the number of quality human resources, improve employment productivity, support influential institutions, and secure sustainable economic advancement [13].
# Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method
## 2 Proposed Methods  
### Complete Teager Operator  
The linear harmonic oscillator with no decaying free oscillations can be defined as follows:  
\[m\ddot{y}+p\dot{y}=0. \tag{1}\]  
Its general solution can be described as \(y(t)=Acos(\omega*t+\theta)\). The trend change of the narrowband signal can be analyzed and tracked by simple mathematics. The discrete expression of the energy track operator \(\psi\) is defined as:  
\[\psi_{c}[y(n)]=[y(n)]^{2}-y(n-1)y(n+1) \tag{2}\]  
\(\psi_{c}[y(n)]=A^{2}\text{sin}^{2}\omega\) can be obtained by substituting \(y(n)\) into Equation (2) and it can localize the change in impact energy. The complete Teager energy operator (CTEO) is defined as \(T\):  
\[T[y(n)]=\frac{\psi_{c}[y(n)]}{\frac{sin^{2}(\omega)}{\omega^{2}}}=\frac{\psi_ {c}[y(n)]}{sinc^{2}(\omega)}=A^{2}\cdot\omega^{2} \tag{3}\]  
Obviously, \(T\) is the square product of the instantaneous amplitude of the vibration and its instantaneous frequency. Given that \(sinc^{2}(\omega)<1(\omega\
eq 0)\), so \(T\) is larger than the original Teager \(\psi\). In contrast to the traditional definition of energy, it increases the product of the frequency square. Because of the high vibration frequency of the transient component, the modulation results of CTEO have the ability to effectively characterize the transient impact component. It should be noted that the energy operator used in all subsequent experiments is CTEO.  
\[\left.\begin{array}{l}y(t)=y_{1}(t)+y_{n}(t)\\ y_{1}(t)=\sum_{j}A_{j}p(t-jT-\tau_{j})\\ p(t)=e^{-\mathrm{Cf}}\mathrm{sin}(2\pi f_{n}t)\\ A_{j}=1+A_{i}\mathrm{sin}(2\pi f_{i}t)\end{array}\right\} \tag{4}\]  
Equation (4) describes the simulation bearing fault-induced signal. Here, \(A_{i}\) denotes the initial amplitude. \(A_{j}\) represents the signal modulation amplitude. \(\tau_{j}\) represents a slight time shift for each period. C is the damping coefficient of signal oscillation. \(f_{n}\) denotes the resonance frequency and \(f_{r}\) is the rotation frequency. \(T\) stands for cycle period. By choosing appropriate values of the parameters, it is possible to simulate the bearing fault signal at low speed. In this test, the detail parameters are selected as C = 750, \(f_{r}\) = 1 Hz, \(f_{n}\) = 3000 Hz, and \(A_{0}\) = 0.5. Moreover, the signal sampling rate \(f_{s}\) is 12 kHz and the acquired data points are \(L\) = 12,000. The time-domain waveform of simulated fault signals is shown in Figure 1.  
Figure 1a shows the simulation bearing a fault-induced signal. The repetition period is 20 Hz, maximum amplitude is 0.5 V, sampling frequency is 12 kHz, and the data points are 24,000. Figure 1b shows the impulse signal with white noise; its SNR = 10.1260 dB. Figure 1c shows the signal enhanced by the TEO with SNR = \(-\)16.3939 dB. The experimental results show that the TEO can enhance the impulse magnitude and effectively improve the SNR of the impact component in the signal.
# Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks
## 4 Evaluation
### Traffic Model  
Network virtualization is expected to be used in a wide variety of situations and scales in the IoT scenario, resulting in unforeseeable traffic patterns [1; 2; 44; 49; 50]. Consequently, this study deals with a basic traffic model for performance evaluation, and the packet processing is designed on the basis of the \(M/D/1/K\) queuing model [51]. The traffic condition changes discretely every time unit. Here, the average arrival rate (i.e., the rate at which a new packet is generated on a V-node at time \(t\)) is expressed by \(\lambda\). The destination V-node of the generated packet is selected randomly from the other V-nodes on the VN to which the V-node belongs.  
The routing path for a packet is determined so as to minimize the total number of P-links from the source V-node to the destination V-node. If there are multiple candidates for the shortest path, then one of them is selected randomly. For a pair of V-nodes, each path is determined statically and then left unchanged during the simulation. The total packet delay consists of the propagation delay and the queuing delay. A packet sent from a V-node at time \(t\) arrives at the next V-node at time \(t+1\) because of the propagation delay (i.e., the propagation delay on each P-link is 1). Packets newly generated and packets arriving from neighbors at time \(t\) on a V-interface are stored on the buffer immediately. Because of the limitation of the bandwidth of P-links, the maximum number of packets that can be sent from one P-interface at time \(t\) is set uniformly to \(W\), resulting in the queuing delay. Every P-interface holds a packet buffer that can store \(\mathcal{B}^{P}_{r,s}\) packets, and packets waiting for their turn to be sent out are stored in the buffer. Packets are basically processed in a first in, first out (FIFO) manner, but the sending order is determined randomly when a V-interface receives multiple packets simultaneously. When a packet arrives at an intermediate full buffer (i.e., \(\sigma^{V_{k}}_{r,s}(t)=0\)), it is re-transmitted from its source V-node at the next time step. Packets are removed from the VN after either being re-transmitted \(R\) times or arriving at the destination V-node.
Sensors_**2013**, _13_, 8239-8284; doi:10.3390/s130708239  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
D-MSR: A Distributed Network Management Scheme for Real-Time Monitoring and Process Control Applications in Wireless Industrial Automation  
**[PERSON] *, [PERSON] and [PERSON]**  
Pervasive Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, P.O. Box 217, Enschede 7500 AE, The Netherlands;  
E-Mails: [EMAIL_ADDRESS] (A.D.); [EMAIL_ADDRESS] (P.H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +31-53-489-3798.  
_Received: 8 April 2013; in revised form: 7 May 2013 / Accepted: 12 June 2013 / Published: 27 June 2013_  
**Abstract:** Current wireless technologies for industrial applications, such as WirelessHART and ISA100.11a, use a centralized management approach where a central network manager handles the requirements of the static network. However, such a centralized approach has several drawbacks. For example, it cannot cope with dynamicity/disturbance in large-scale networks in a real-time manner and it incurs a high communication overhead and latency for exchanging management traffic. In this paper, we therefore propose a distributed network management scheme, D-MSR. It enables the network devices to join the network, schedule their communications, establish end-to-end connections by reserving the communication resources for addressing real-time requirements, and cope with network dynamicity (e.g., node/edge failures) in a distributed manner. According to our knowledge, this is the first distributed management scheme based on IEEE 802.15.4e standard, which guides the nodes in different phases from joining until publishing their sensor data in the network. We demonstrate via simulation that D-MSR can address real-time and reliable communication as well as the high throughput requirements of industrial automation wireless networks, while also achieving higher efficiency in network management than WirelessHART, in terms of delay and overhead.  
**Keywords:** distributed scheduling; real-time WSANs; WirelessHART; ISA100.11a; IEEE 802.15.4e
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### LTE-A Delay
#### 4.1.1 Control Plane
\begin{table}
\begin{tabular}{c c c} \hline
**Step** & **Description** & **Time (ms)** \\ \hline
1 & RACH scheduling period & 0.5 \\
2 & RACH preamble transmission & 1 \\
3–4 & Preamble detection and transmission of RA response & 3 \\
5 & UE processing & 5 \\
6 & Transmission of RRC Connection Request and Non-Access & 1 \\  & Stratum (NAS) request & 1 \\
7 & eNB processing (I2 and RRC) & 4 \\
8 & Transmission of RRC Connection Setup (+ UL grant) & 1 \\
9 & UE processing (I2 and RRC) & 12 \\
10 & Transmission of RRC Connection Set-up Complete & 1 \\
11 & eNB processing (Uu\(\rightarrow\)SI-C) & \\
12 & S1-C transfer delay & \\
13 & Mobility Management Entity (MME) Processing Delay & \\
14 & S1-C Transfer delay & \\
15 & eNB processing (S1-C \(\rightarrow\) Uu) & 4 \\
16 & Transmission of RRC Security Mode Command and Connection & 1.5 \\  & Reconfiguration (+TTI alignment) & 1.5 \\
17 & UE processing (L2 and RRC) & 16 \\  & **Total** & **50** \\ \hline \end{tabular}
\end{table} 
Table 3: Radio Resource Control (RRC) connection establishment in Long-Term Evolution Advanced (LTE-A) [32]. Reconfiguration complete message to the target cell, and the target cell resumes data transmission. It should be noted that LTE supports dual connectivity, where the UE simultaneously connects to two BSs. This may enable uninterrupted service continuity during an HO, at a penalty of using additional resources.
## 2 Results
### Volatile Compounds in Proso Millet Wine
#### 2.2.3 Carbonyl Compounds  
Carbonyl compounds in the samples included aldehydes and ketones. In the proso millet wine liquor sample, five aldehydes and five ketones were detected using SPME with different fibers. Samples showed relatively high concentration of aldehydes, with a minimum of 4622.73 \(\pm\) 314.44 \(\mathrm{\SIUnitSymbolMicro g}\)/L in PAcontaining four aldehydes and a maximum concentration of 33852.25 \(\pm\) 2457.00 \(\mathrm{\SIUnitSymbolMicro g}\)/L in CAR/PDMS containing five aldehydes. Benzaldehyde and benzeneacetaldehyde were detected in all fibers. PA and CAR/PDMS showed a good detection effect for other aldehydes, such as benzenebutanal and ethylidene-benzeneacetaldehyde. Most ketones were detected in somewhat less amounts than aldehydes in different fibers. There were greater adsorption differences when different fibers were used. Dihydro-5-pentyl-2(3H)-furanone, benzophenone, and 1,1-diphenyl-2-propanone only were detected in PA; acetophenone only was detected in CAR/PDMS, and octan-3-one was only detected in PDMS and DVB/CAR/PDMS. Carbonyl compounds were also common odor components in rice wine. Benzaldehyde and octan-3-one were detected in rice wine [6; 9]. Aldehydes and ketones are believed to result from the direct oxidation of their corresponding alcohols and fatty acids, respectively [31]. Other authors suggested that carbonyls result from the degradation of amino acids and sugar [32]. On the basis of the OAV, the most potentially important aroma aldehyde was benzeneacetaldehyde (OAV ranging from 56.7 to 214.47), which imparted a sweet and floral aroma. Benzaldehyde may be the second most important aroma aldehyde, which had a OAV ranging from 10.8 to 93.1 and provided sweet and fruity to the sample. In the detected ketones, octan-3-one, which is known for its fruity odor, had a high OAV in CAR/PDMS (9.6) and DVB/CAR/PDMS (18.1) fiber coatings.