esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Vibration Data Analysis: Definition of the RWEE Damage Index
### Wavelet Entropy (WE)  
Wavelet entropy is a combination of wavelet decomposition and entropy statistics theories. This concept presents the advantages of multi-resolution analysis and complexity evaluation for time-varying signals, which means that the macro and micro aspects of some special signals could be researched in the time-frequency domain [48]. For this reason, wavelet entropy has gradually been adopted for a great variety of applications in medicine, machinery vibration detection, and system fault diagnosis [35; 36; 40; 41].  
Figure 4: Schematic representation of three Wavelet Packet Transform (WPT) levels of a signal.  
Damage is shown as a disorder at the structural response signal. One natural approximation to quantify the order level of a complex signal is through spectral entropy or Shannon entropy, defined from the Fourier power spectrum [37].  
Shannon entropy can be calculated using the formula  
\[E=-\sum_{i=1}^{N}p_{i}\cdot log(p_{i}) \tag{7}\]  
where \(p_{i}\) is the probability that a system is in the state \(i\) and \(N\) is the total number of states.  
Wavelet entropy is used to characterize the degree of order/disorder associated with a signal of multifrequency response. Wavelet energy entropy is based on the relationship between the concept of entropy and the energy obtained from each frequency band of the wavelet decomposition, as  
\[WE=-\sum_{i=1}^{N}\frac{E_{i}}{E_{t}}\cdot log\left[\frac{E_{i}}{E_{t}}\right] \tag{8}\]  
where \(E_{i}\) is the energy of each node and \(E_{t}\) is the sum of the energy of all considered nodes.
## 4 Simulation and Experimental Verification  
### Multi-Objective Optimal Design Option Strength Verification  
The optimal design option from the multi-objective optimization was verified through modal vibration analysis, dynamic test simulation, bench testing, and road excitation. The corresponding parameters of each component are listed in Table 10.
# Tor Hidden Services: A Systematic Literature Review
## 2 Tor Hidden Services in a Nutshell
### Tor  
Tor [1] is a distributed overlay network, composed of a set of nodes named onion routers (OR) or relays, to anonymize TCP-based applications. When a client wants access to a TCP-based service, such as the Web or instant messaging, he has to build a path to exchange information with the service.  
In Figure 1, we show a connection between a client and a website. To create this connection, the client obtains a list of Tor nodes from the directory (step 1). From the list,in most cases, he chooses three nodes for building the circuit: an entry guard (it could also be a bridge, which is a relay that is not publicly listed in the directory), a middle relay, and an exit relay.  
The process of building the circuit is summed up next. This is an incremental process where the client, first, creates a circuit with the entry guard (step 2). For creating this circuit, a TLS connection between the client and the entry guard is established where they negotiate, using a Diffie-Hellman handshake, a key to exchanging information within the TLS connection. The information exchanged in the circuit is based on a data structure named _cell_, whose size is fixed at 512 bytes. Next, the client requests the entry guard to extend the circuit to the middle relay (step 3). In this process, the client negotiates a key with the middle relay in the same way he did it with the entry guard. Finally, the client extends the circuit to the third relay (step 4), the exit relay, which will be used to send the client's traffic to the website. The way the circuit is extended to include the exit relay is the same used to extend the circuit in the previous step. Thus, the client has established a path with three nodes, and with each node has negotiated an encryption (session) key. It is also important to point out that any relay in the path only knows its predecessor and successor, but no the rest of the relays in the circuit.  
Once the circuit is built, the client can exchange information with the website (step 5). From the website's point of view, the connection comes from the exit relay--that is, who is sending the data received from the client through the circuit. These exchanges are sent through the path established by means of relay cells. In each cell, the client sends her data through relay cells. The data are within the payload, which is encrypted with the key negotiated with each relay. Namely, the payload is ciphered, first with the exit relay key. Next, the result is ciphered with the middle relay key, and finally, the result is ciphered with the guard relay key. As we can see, there are several layers of encryption, and in each relay, a layer of encryption is removed. This is the base of the onion ciphering. It is also important to point out that in each circuit we can multiplex many TCP streams. Thus, we could use it to connect to several websites or other services, such as instant messaging.  
A more detailed description of Tor, its features, and the protocol can be found in [1].  
Figure 1: Tor circuit. Source: [19].
## 2 Materials and Methods  
### Study Area  
This study acquired MODIS satellite NDVI data at a 250 m spatial resolution. Considering the limitations posed by mixed pixels (e.g., roads and buildings) at maize-planted flux sites within this resolution, the research focused on data from four maize flux sites in America (Figure 1). Data of the NE1, NE2, NE3, and ROI sites were downloaded from Fluxnet 2015 ([[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)), accessed on 10 June 2020). The ROI site is situated in Minnesota, while the other three sites, NE1, NE2, and NE3, are all located in Nebraska. The NE1, NE2, and NE3 sites are in close proximity to each other, with a distance of 1.6 km between them. The NE1 site primarily grows maize in continuous year, while the NE2 and NE3 sites have a maize-soybean rotation. The NE1 and NE2 sites are irrigated, whereas the NE3 site is completely rainfed. The three sites in Nebraska have comparable yearly temperatures and deep silt clay soils. Due to water stress in NE3, the maize planting density is lower compared to the NE1 and NE2 sites. Furthermore, the NE1 and NE2 sites have sufficient soil moisture, ranging from 0.27 to 0.31, while NE3's soil moisture is below 0.19 [49]. The details of the NE1, NE2, NE3, and ROI sites are listed in Table 1. The locations of these sites and their corresponding MODIS footprints at a resolution of 250 m are shown in Figure 1. The mixing phenomenon in the MODIS pixel including the flux site is unobvious. This ensures the rationality of the following analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Longitude (\({}^{\circ}\)W)** & **Latitude (\({}^{\circ}\)N)** & **Available Year** & **Data Size** \\ \hline NE1 & \(-\)96.4766 & 41.1651 & 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012 & 1945 \\ NE2 & \(-\)96.4701 & 41.1649 & 2001, 2003, 2005, 2007, 2009–2012 & 1276 \\ NE3 & \(-\)96.4397 & 41.1797 & 2001, 2003, 2005, 2007, 2009, 2011 & 987 \\ RO1 & \(-\)93.0898 & 44.7143 & 2009, 2011 & 214 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the available daily data at four flux sites.
###### Abstract  
In view of the fact that field monitoring information can more intuitively and accurately reflect the stability state of slopes, this paper takes the warning threshold of slope deformation rate monitoring as the slope stability evaluation standard, and puts forward a method for solving the safety coefficient of rocky slopes. The discrete element method (3 DEC), which is suitable for rocky slopes, is selected as the numerical calculation tool, the convergence criterion of its strength reduction method is modified to the slope deformation rate threshold, and the method is realized by the bifurcation method through its built-in FISH programming language. The results of the classical case show that, by selecting the slope deformation rate threshold in the appropriate interval, the results of this paper's method are very close to those of the finite unit stress method and the limit equilibrium method, verifying the reliability of this paper's method. Further, the method of this paper is applied to an open-pit mine slope project, based on the slope deformation on-site monitoring data and through the time series prediction method to determine the slope deformation rate warning threshold, using the threshold as an evaluation criterion to solve the slope's coefficient of safety. The calculation results show that the slope's coefficient of safety in natural working conditions is 1.086, being basically stable. However, with continuous rainfall, the slope's body gradually becomes saturated, the slope's coefficient of safety is reduced to 0.987, and the slope's safety is reduced to 0.987. After continuous rainfall and gradual saturation of the slope, the coefficient of safety decreases to 0.987, resulting in destabilization and destruction, which is consistent with the site conditions.  
rocky slopes; safety factor; strength reduction method; monitoring and warning; deformation rate
## 6 Conclusions and Future Work
The choice of the C-band guarantees an improvement of the antennas gain and consequently the possibility to detect objects at greater distance still. A PAF is made up of closely packed antenna elements that, by spatially sampling the focal plane, can synthesize multiple independent beams. Beam shapes and directions are controlled electronically by weighting the amplitudes and phases of the signals applied to the individual antennas by a beam-former [35; 36; 37; 38]. Consequently, through the beam-forming process, PAFs are able to synthesize multiple beams and optimize each of them, enhancing aperture efficiency as well as effective FOV. The beam shapes and side lobes can be modified in real time and be set to minimize their response, in relation to undesired radio frequency interferences. Conceptualization, L.S. and T.P. ; methodology, L.S., T.P. and A.P. ; software, A.P. ; validation, L.S., T.P. and A.P. ; formal analysis, L.S., T.P. and A.P. ; investigation, L.S., T.P. and A.P. ; resources, L.S., T.P. and A.P. ; data curation, L.S. ; writing--original draft preparation, L.S. ; writing--review and editing, L.S. ; visualization, L.S., T.P. and A.P. ; supervision, L.S. and T.P. ; project administration, T.P. ; funding acquisition, T.P. All authors have read and agreed to the published version of the manuscript. This work was founded in part by the European Commission Framework Programme H2020 and Copernicus Space Surveillance and Tracking under grants 785257-2-3 SST2016 and 237/G/GRO/COPE/16/8935-ISST2016. Data sharing is not applicable. The authors are grateful to [PERSON], for his precious suggestions in antennas coordinates pointing calculation. The authors declare no conflict of interest.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### Problem Formulation  
This section investigates the problem formulation of our system, in which reducing the total energy overhead for a multi-level blockchain-enabled MEC system for V2X networks is the main goal. Therefore, based on the above models (i.e., communication and computation), the models can be formulated as a constrained optimization, as follows:  
\[\begin{array}{ll}\min_{\alpha}&\sum_{i=1}^{M}E_{i}\\ &E_{i}-E_{i}^{l}\leq 0,&\text{C1}\\ &T_{i}-T_{i}^{l}\leq 0,&\text{C2}\\ &\sum_{j=0}^{N+1}\alpha_{i,j}=1,&\text{C3}\\ &\alpha_{i,j}\in\{0,1\}&\text{C4}\\ \end{array} \tag{13}\]  
This optimization problem aims to reduce the energy consumption of the entire system through task offloading. In addition, the upper limit for energy consumption and time are addressed through the first two constraints (i.e., C1 and C2, respectively), whereas the execution limit (i.e., only one time) for each vehicle's \(i\) task is guaranteed via constraint C3. Finally, the binarization of the offloading decision variable is guaranteed through constraint C4.  
In the case of linear objective functions and constraints, the problem solution is determined through finding the best value for the decision offloading variable \(\alpha^{*}\). Therefore, the branch and bound technique was used in this study, in accordance with [39; 40], to solve this problem.  
Problem Solution Using Energy-Efficient Task Offloading (EETO) Algorithm  
This subsection describes our energy-efficient task offloading (EETO) algorithm to determine the optimal offloading decision of a multi-level blockchain-enabled MEC system for V2X networks. First, each RSU sends an information summary of their vehicles, including vehicles' number, available computation resources and transmission rate, to the core network. In addition, each vehicle also send the tasks' requirements, including tasks' CPU cycles, input size and transmission power, to the core network. Next, based on Equation (13), the core network can derive the optimal offloading decision for each vehicle's task through the problem solution. Finally, each RSU sends the offloading decision for each vehicle which determines wherever the task is to be executed (i.e., locally, edge or cloud server). The detailed process for energy-efficient task offloading is outlined in Algorithm 1.
# Study on Few-Shot Object Detection Approach Based on Improved RPN and Feature Aggregation
## 3 Definitions and Improved Network Framework
### Basic Network Framework
#### 3.2.1 Feature Extractor  
We use the RestNet-101 network pre-trained on the ImageNet dataset for feature extraction of input images. For a given image \(i_{q}\in R^{H_{q}\times W_{q}\times 3}\), use RestNet-101 to extract CNN features. So, we have \(F_{q}^{c}=F(i_{q})\in R^{H\times W\times C}\). Here, \(H\), \(W\), and \(C\) represent the height, width, and number of channels of the extracted feature dimensions, respectively. We choose the output after the res4 block of RestNet-101 as the feature of the image. For supporting images, crop the corresponding object regions in the image, adjust the cropped image to the same size, and then input it into the shared feature backbone network to extract the features of the supporting image. Using the query image features \(F_{q}^{c}\) and the supporting image features \(F_{s}^{c}\) of \(c\in C\) as inputs, generate query features \(i_{i}^{c}\) for specific classes. The main process of the improved RPN and feature aggregation few-shot object detection algorithm IFA-FSOD proposed in this paper is as follows: Firstly, the supporting image and query image are input into the feature extractor to extract features. In RPN, a similarity matrix is established between the support features and anchor box features of a new class, and the similarity between the two is calculated to obtain candidate regions with high IoUs. Finally, a feature aggregation module is introduced in RoI Align to effectively aggregate features generated from RoI of different scales. Normalize in the final softmax layer to fuse the information of each feature more evenly and obtain the final object detection result.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 2 Problem Formulation  
The main objective of the MSDTNEP problem is the minimization of the total investment cost and expected energy not supplied subjected to a set of constraints over the entire planning horizon. The constraints are the power flow equations, line limit equations, and N-1 security.  
Due to the TNEP problem's complexity, the DCPF model was used in the TNEP models applied in this study. The main assumptions in the traditional DCPF model are: network loss is negligible, line resistance (active power loss) is negligible or insignificant (i.e., \(R<<X\)), and magnitude of bus voltage is set to 1.0 per unit (flat voltage profile), where R is the line resistance and \(x\) is the reactance. Based on these three assumptions, active power injection at bus \(i\) (\(P_{i}\)) is obtained as follows [4]:  
\[P_{i}=\sum_{j=1}^{N}B_{ij}\left(\theta_{i}-\theta_{j}\right). \tag{1}\]
# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 2 Virtual Power Plant (VPP)  
VPPs aim to achieve better use of normally spatially dispersed energy resources, and to coordinate their joint operation to meet energy demand requirements [1]. They present a set of generation units, controllable loads, and network-dispersed energy storage systems and aggregates [10] to operate as a single plant [11] for the system operator, as shown in Figure 1.  
The representation in the known literature of a VPP connected to the system presents Distributed Generators (DG). These have different generations (G) and different load profiles (L), and are controlled by a VPP that presents a system operator through an aggregator [13] with a single operational profile of a parameter composed of the whole system, which characterizes each DER and each load. These make it possible to act directly on market scenarios through energy supply and demand, improving reliability and price fluctuations in daily or real-time markets.  
Some key components of a VPP's topology include the establishment of information connections and the changing of energy resources along with the energy market. Figure 2 represents the sets of different non-renewable and renewable storage generators and storage devices that can be integrated. Some perform commercial roles, such as bringing together the energy production of several local units and marketing them as a single entity. Others  
Figure 1: Concept representation of a VPP (adapted fenix-project [12]).  
perform more technical operations, such as being able to adjust the production profile of their generator components or even providing ancillary services to the system.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
###### Abstract  
Precise altitude data are indispensable for flight navigation, particularly during the autonomous landing of unmanned aerial systems (UASs). Conventional light and barometric sensors employed for altitude estimation are limited by poor visibility and temperature conditions, respectively, whilst global positioning system (GPS) receivers provide the altitude from the mean sea level (MSL) married with a slow update rate. To cater to the landing safety requirements, UASs necessitate precise altitude information above ground level (AGL) impervious to environmental conditions. Radar altimeters, a mainstay in commercial aviation for at least half a century, realize these requirements through minimum operational performance standards (MOPSs). More recently, the proliferation of 5G technology and interference with the universally allocated band for radar altimeters from 4.2 to 4.4 GHz underscores the necessity to explore novel avenues. Notably, there is no dedicated MOPS tailored for radar altimeters of UASs. To gauge the performance of a radar altimeter offering for UASs, existing MOPSs are the de facto choice. Historically, frequency-modulated continuous wave (FMCW) radars have been extensively used in a broad spectrum of ranging applications including radar altimeters. Modern monolithic millimeter wave (mmWave) automotive radars, albeit designed for automotive applications, also employ FMCW for precise ranging with a cost-effective and compact footprint. Given the technology maturation with excellent size, weight, and power (SWaP) metrics, there is a growing trend in industry and academia to explore their efficacy beyond the realm of the automotive industry. To this end, their feasibility for UAS altimetry remains largely untapped. While the literature on theoretical discourse is prevalent, a specific focus on mmWave radar altimetry is lacking. Moreover, clutter estimation with hardware specifications of a pure look-down mmWave radar is unreported. This article argues the applicability of MOPSs for commercial aviation for adaptation to a UAS use case. The theme of the work is a tutorial based on a simplified mathematical and theoretical discussion on the understanding of performance metrics and inherent intricacies. A systems engineering approach for deriving waveform specifications from operational requirements of a UAS is offered. Lastly, proposed future research directions and insights are included.  
UAS; FMCW; mmWave; automotive; radar; altimeter +
Footnote †: journal: Advances in Applied Mechanics and Engineering  
1
Footnote 1: [[https://www.mdpi.com/journal/drones](https://www.mdpi.com/journal/drones)]([https://www.mdpi.com/journal/drones](https://www.mdpi.com/journal/drones))
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Packet Formulation
#### 3.4.1 Raw Data  
ChipSats transmit data synchronously in packets that include data from each sensor on board. This meets requirement 2.0, which is to periodically record IMU data (see Table 4). Also, packet transmission enables the sample data to have a single timestamp and a complete dataset for each transmission. A benefit of using a single timestamp is that it allows for consistent parsing of the data from each transmission. Since ChipSats do not have unique identifiers for each sensor, knowing the chronological order of the datasets can help recognize each component. The data can be identified based on the expected transmission order. Figure 19 below shows how the packet is made from the IMU values on the ChipSat.  
The packets were initially made to only send the IMU data for simplicity. The packets begin by sending three gyroscope values for each direction, and then the accelerometer and magnetometer values (see requirement 2.2 in Table 4). There are nine values that would be transmitted in total. Later, we will add data from the GPS and a temperature sensor. For each of the nine transmitted values, we wanted 16-bit resolution, as per requirement 2.1 (see Table 4). Since the RTOS was written to send the packet in 8-bit pieces, each of the values was divided into two halves. So, 18 8-bit data transmissions are sent in total.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 6 Two Illustrative Examples  
To showcase the practical application of the monitoring scheme proposed in this paper, we employ two diverse datasets. The first dataset originates from OLED production, where precise monitoring is crucial for maintaining high-quality standards in advanced display technology [37]. The second dataset comprises data from mining accidents, highlighting the importance of effective monitoring in safety-critical environments to promptly detect anomalies and prevent potential disasters [38].
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 3 Results and Discussion
### Surface Morphology Evolution
This series of phenomena indicates that surface defects from the previous layer had a significant impact on the formation of subsequent layer surfaces, and continuous multi-layer printing made it difficult to repair the rough surfaces formed by previous layers [30]. Our study integrates co-axial melt pool monitoring data to investigate the formation mechanisms of typical surface morphological features such as reticulated structures, large-scale local agglomerations, and edge contour protrusions on intermediate layers. It analyzes the causes of transitional surface formation based on the temperature field distribution within the melt pool and discusses the microstructural changes in uniform surfaces based on surface roughness measurement results.