esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Results and Insights
#### 5.4.3 Topology and Service Performance Metrics  
Only a few reviewed papers consider topological parameters in metrics computation [127; 131; 140; 141] due to the high level of abstraction in graph-based methods and static features therein. Still, it is important to include them in resilience studies because they capture network architecture and internal dependencies between different elements that complement service performance measures. As discussed in Section 5, a noteworthy multi-criteria approach was suggested in [137] to combine topological and operational characteristics in the same metric. Although more inclined toward topological features, this proposal illustrates how multiple weighted parameters can be aggregated into a single representative indicator. In addition, interdependence modeling widely adopts topological approach [154; 155], so it is unavoidable to embrace it in power systems, because in the long run, smart grids resilience must be analyzed taking into account the interactions between electric and ICT layers; and with other infrastructure networks (gas, heat, cooling, transports, etc.).  
Like transmission power networks [29], multiple metrics are proposed for resilience quantification in [124; 128; 131; 135; 142]. A single resilience metric, even in the case where it embeds a maximum number of resilience features, can represent a drawback if it offers less information for enhancement strategies implementation. The reason is apparent in some strategies that only target one facet of resilience, let us say for example robustness; hence, when the metric combines many features it dilutes the information about robustness in the general index. This is why multiple metrics, each handling an aspect or phase of resilience, can help to build better knowledge and guide more specific actions.
## 1 Introduction
### Contribution and Content of the Paper  
The distribution of parathyroid glands in SPECT volume varies in patients. There are numerous problems for manual or automatic determination of spatial properties of parathyroid glands:  
* SPECT images are very noisy and low-pass filtering leads to blurred images with loss of some details.
* Planar images are straightforward for analysis, but few details are lost due to single view projection as detail overlapping occurs.
* Volumetric images are difficult to analyze for a human operator due to small and low contrast objects.
* Numerous details are recognized after careful examination of SPECT volume, but the automatic estimation of parameters is necessary.  
Available techniques for the improvement of visual inspection of parathyroid glands include: dedicated extended color palette, small VOI selection using sphere, and background threshold-based rejection [27]. The most crucial problem is the analysis of cases where parathyroid glands with small distances between them are observed. Observed local maxima in the SPECT volume are dispersed as 3D distributions, therefore overlapping occurs. Automated estimation of parameters for distributions could be valuable for research purposes and further design of a Computer-Assisted Diagnosis system (CADx). This paper assumes the Analysis by Synthesis approach with the use of a complex model and determination of unknown parameters by the application of particle swarm optimization (PSO) [28; 29; 30].  
Real SPECT measurements were used for the determination of model type and approximate value ranges. In this paper, SPECT patient data was deliberately not used, because the quality of estimation cannot be calculated this way. An alternative approach that was proposed in [31], which is based on a comparison of synthetic volume, and achieved from an estimation algorithm (synthesized) is the only possible method for the proper evaluation of the optimization algorithm. Such an approach with the use of Monte Carlo sampling allows the determination of estimation quality parameters. This is a very challenging task since small volumes with significant overlapping are difficult to analyze due to nonlinearity and large number of local minima. The considered approach allows parallel processing to achieve a reasonable processing time.  
Synthetic data sets used in this work are described in Section 2. The method for the estimation of parameters is described in Section 3. Comparative results, based on Monte Carlo tests for proposed PSO are presented in Section 4. The discussion is provided in Section 5. Final conclusions and further work are presented in Section 6.
## 3 FEA Model and Validation  
### Overview of Finite Element Model  
The same FEA model is adopted in this paper as in a previous review [34]. The FEA model is generated to explore the variation of soil responses and lining internal forces during the twin-tunneling process using the URUP method. The commercial nonlinear FEA code ABAQUS is employed in the numerical simulation. The dimension of the FEA model (see Figure 5) is 108.0 (length) \(\times\) 59.6 (width) \(\times\) 40.0 m (depth). A total number of 100,680 nodes and 84,869 elements are employed in the FEA. The solid element type of C3D8P (8-node trilinear displacement and pore pressure) and C3D6P (6-node linear displacement and pore pressure) are employed for modeling the soils, considering pore-water-pressure variation, which is an essential factor influencing the soil consolidation process [42]. For grout layer and tunnel lining, a total number of 4864 and 4593 C3D8I (8-node linear brick, incompatible modes) elements are adopted in the FEA, respectively. Displacement boundaries are configured with the model fixed vertically at the bottom and horizontally at the four vertical boundaries. The soil, grout, and lining are tied together during the FEA. The \"element death\" technique is employed to simulate the excavation process.
## 2 Separation Methods for Characterization of Balsamic Vinegars
### Liquid Chromatography  
In 1952, with the advent of gas chromatography, [PERSON] and [PERSON] suggested the possibility of using a liquid mobile phase for rapid separation, similar to GC. To do this, it was necessary to achieve a small particle size of the stationary phase and the use of pressure on the mobile phase. In 1960, [PERSON], [PERSON], and [PERSON] reported the use of liquid chromatography (LC) for amino acid separation, unfortunately, without the wider attention of other scientists. HPLC was therefore introduced in the late 1960s, when [PERSON], [PERSON], and [PERSON] published their work about this method, dealing with the development of uniformed small-particle sorbents [72].  
Figure 4: Analysis of VOCs in TBV by GC-MS method. Figure adapted from [71].  
High-performance liquid chromatography is applicable for the separation and analysis of complex chemical mixtures into individual components. Thanks to its almost universal use, it is one of the most widely used analytical methods, especially in food analysis. The most commonly used is the reversed-phase mode, where the stationary phase is less polar than the mobile phase. Gradient elution is often used to speed up the whole analysis process, using two mobile phases (one polar and the other less polar), whose ratio changes during the process [73].  
For the application in the food analysis area, the HPLC is typically used for analyses of (poly)phenolic compounds, which usually enter food products as secondary plant metabolites, and exhibit potent antioxidant properties. Together with other non-volatile compounds (such as sugars, amino acids, or amines), the full characterization of complex food samples is sometimes difficult with conventional HPLC, and therefore the application of innovative approaches, for example, multidimensional liquid chromatography [74], or the application of highly efficient columns packed with porous shell particles can be essential [75]. For the study of individual compounds contained in plant products, HPLC is mostly combined with UV/Vis, DAD, and MS detection (Table 2).
## 4 The Experiment on the SkeL Platform  
The platform has been used and tested in a large number of experiments. Thus, for instance, in the work described in [16], crowdsensing and crowdsourcing have been combined in order to identify and localise WiFi networks. In the work described in [5], the platform has been used to implement two hybrid human-machine workflows for solving two mobility problems: modal split estimation and mapping mobility infrastructure. Last but not least, the platform has been used in a large scale personal data collection experiment, run by Eurostat, aimed at feeding a Europe-wide big data hackathon [17]. In this paper, we concentrate on one of a series of MCS experiments run at the University of Trento whose goal was to study the effects of students' behavior and habits on their academic performance. The main reason for this choice relates to the fact that, in the case of personal data, it is easier to collect feedback, as this activity involves only the user of each single device.  
The highlights of the experiment are as follows. Out of an initial population of 312 students, 72 were selected who satisfied three specific criteria, namely, their willingness to share personal information, the fact that they were actively attending classes, and that their phone could support the i-Log application. The cohort was designed to reflect the general population of students in terms of gender and field of study. The experiment lasted two weeks. During the first week, students were asked to answer a specific questionnaire every half an hour, and the answers were the labels used by SkeL. Meanwhile, i-Log would keep collecting the sensor data from their smartphone. During the second week, students only needed to keep the application running in the background for the sensor data collection. The sensors used in the experiment are those listed in Table 1. The resulting dataset has a total size of 110 GB, containing 20 billion sensor values plus the user answers to the time diaries' questions, which are considered as an annotation to the sensor data. The total number of answers generated during the experiment is 17,207 out of 27,111 sent.  
Table 5 shows the questionnaire that users had to answer during the first week. The design of the questionnaire follows the context model from [18]. It is composed of three questions, namely \"What are you doing?\", \"Where are you?\", and \"Who is with you?\". Notice that, if the user's answer of the activity was \"En route\", the user was further asked about the transportation mode. The user could choose, as his/her answer, one of a pre-selected list of labels. The user's answers were meant to help in the understanding of the user situational context and its aspects relating to his/her activity, location, and social relations.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
### Contributions
#### 1.1.1 Contribution 1  
This document shows the feasibility of using a random variable to characterize the elevation angle behavior for LEO satellites with different orbit configurations. The suitability of using the proposed random variable is verified through an extensive error analysis with diverse orbit configurations.
# Geophysical Study of a Large Landslide Affecting the Urban Area of Albuuuelas (S Spain)
## 3 Geophysical Methods
### f-k Analysis  
The method based on the frequency-wavenumber (_f-k_) analysis [20; 21] is applied on seismic array measurements for estimating the propagation velocity of the Rayleigh waves as a function of the frequency (i.e., dispersion curve).  
The _f-k_ analysis estimates the power spectral density function of the recorded seismic noise in the frequency-wavenumber domain. This function describes the phase and amplitude of the plane waves propagating in the _x-y_ plane of the array with velocities and directions defined by the two-dimensional horizontal wavenumber vector \(\mid\stackrel{{\rightarrow}}{{k}}\mid,(k_{x},\ k_{y})\). Specifically, considering the pulsation of the plane waves, \(\omega=2\pi f\), the wavenumber vector \(\mid\stackrel{{\rightarrow}}{{k}}\mid\), is related to the phase velocity \(\stackrel{{\rightarrow}}{{v}}\) and the propagation angle \(\beta\) of the signal by Equation (2):  
\[\begin{array}{l}\left|\stackrel{{\rightarrow}}{{v}}\right|= \frac{\omega}{\mid\stackrel{{\rightarrow}}{{k}}\mid}=\frac{ \omega}{\sqrt{k_{x}^{2}+k_{y}^{2}}}\\ k_{x}=\frac{\omega}{v_{x}}=\frac{\omega\cdot\cos(\beta)}{\left|\stackrel{{ \rightarrow}}{{v}}\right|}\\ k_{y}=\frac{\omega}{v_{y}}=\frac{\omega\cdot sin(\beta)}{\left|\stackrel{{ \rightarrow}}{{v}}\right|}\\ \beta=arctan\left(\frac{k_{y}}{k_{x}}\right)\end{array} \tag{2}\]  
For each of the analyzed frequencies, \(\omega=2\pi f\), and the absolute maximum of the power spectral density in the \((k_{x},k_{y})\) plane is identified, which directly provides the phase \(\beta\) and the propagation velocity \(\left|\stackrel{{\rightarrow}}{{v}}\right|\) of the incoming plane waves.  
There are two different approaches to obtain the power spectral density in the \((k_{x},\ k_{y})\) plane: the maximum likelihood method (MLM) [20] and the beam-forming method (BFM) [21]. The last method is the one used in this study.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Causative Attack Methods  
A causative attack, also called a poisoning attack, is an adversarial attack launched while the model is being trained. In this attack, the attacker compromises the training data set by manipulating it or when the ML classifier is trained with limited data and requires additional training data to retrain itself. In this retraining process, the adversary can interfere by introducing incorrect training data. The attacker aims to either degrade the overall performance of the model or target specific training features or classes. This type of attack assumes that the adversary has access to the learning procedure and can influence the training data to deliberately introduce biases or inaccuracies in the model's learning process. Hence, causative attack is a kind of white-box or gray-box attack.
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 3 Experimental Setup
### Model Parameter Settings  
The classifier model consists of a convolutional layer, an LSTM layer, and a fully connected layer for classification. We first define a series of convolutional layers. These convolutional layers are activated by the activation function GELU, and we use Batch Normalization (BN) to speed up the training process and enhance the stability of the model [32]. In the training loop, forward propagation is performed for each batch, losses are computed, backpropagation is performed, and the optimizer is used to update the weights. For the first convolutional layer, the number of input channels is 2, the number of output channels is 128, the convolutional kernel size is 7, the step size is 2, and the padding is 128.  
Figure 7: Multiplayer action sample chart.  
value is 3. For the subsequent three convolutional layers, the number of input channels and output channels is 128, the convolutional kernel size is 3, the step size is 2, and the padding value is 1.  
The whole CNN part builds a feature extractor through these convolutional layers, which helps to extract useful features from the input time series data, which will be fed into the LSTM layer for time series modeling and classification. The parameters of the LSTM layer include settings such as the input dimensions, the hidden layer dimensions, and batch priority. Here, the input and hidden layer dimensions are set to 128. The LSTM layer operates on the sequence dimension, and hence the input tensor must be in the dimensions of batch_size, seq_length, and input_size, where input_size is a dimension of the input features. The input data are adapted to the input format of the LSTM layer through transpose operation after feature extraction by the CNN part. Then, recursive operations are performed on the time step in the LSTM layer. The LSTM layer processes the sequence data by performing recursive operations on the sequence data in the time step to generate the outputs and the hidden states [33]. The LSTM layer generates the outputs and the hidden states at each time step. Here, only the hidden state is used, and the hidden state is averaged over all the time steps to obtain a fixed representation of the sequence. This fixed representation is fed into the fully connected layer for final classification. We divide the data set into 80% training, 10% testing, and 10% validation sets. We set the initial learning rate to 0.0008, the batch size to 128, and the maximum number of training rounds to 100. The optimization function for the network is Adam. We implement this network in PyTorch.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 8 Conclusions  
The end-to-end encrypted email adoption rate is still low, and many users struggle to make use of these tools due to usability issues, lack of understanding, and misconceptions. We conducted a study on 19 participants to understand user perceptions of an end-to-end encryption platform and how they used it to manage access control (expiration times, disabling forwarding, persistent protection, and watermarking) to confidential information, such as medical, tax, and employee information sent via email. Our results show that the tool's emails were perceived as suspicious, especially when they came from unknown senders. After the participants learned about the tool's features and how it works, they were comfortable sharing medical, tax, and employee information with it. The expiration time and disable forwarding features were most useful for preventing unauthorized access and continued access to the three content types. Many participants chose to use persistent protection even though they did not understand how it worked, believing it would provide an additional layer of security and prevent unauthorized access. Watermarking was the least useful feature for the participants, as many were unsure of its usage. Our participants were concerned about data leaks from recipients' devices if they set longer expiration dates. Furthermore, our findings provide practical implications that could help users to share confidential information via end-to-end encrypted communication media.  
Conceptualization, E.A.Q. and Y.J.; methodology, E.A.Q.; investigation and formal analysis, E.A.Q., S.T. and Y.J.; resources, E.A.Q. and M.S.; data curation, E.A.Q., S.T., L.S. and Y.J.; writing--original draft preparation, E.A.Q., Y.J., S.T. and L.S.; writing--review and editing, E.A.Q. and Y.J.; visualization, E.A.Q. and Y.J.; supervision, M.S.; project administration, E.A.Q. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The APC was funded by Illinois State University.  
Not applicable.  
The authors declare no conflict of interest.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 4 UE Receiver PHY for CS
### PSS Search  
The PSS Search is the most important part of CS PHY as it helps the receiver to identify the presence of the SS block. The challenges in the PSS search are unknown symbol boundary, multiple locations since GSCN is unknown, and large transmission bandwidth of 100 MHz out of which SS occupies only 7.2 MHz resulting in large-size correlators.  
To detect the transmitted PSS index, \(\text{PCI\_1}\in\{0,1,2\}\), the cross-correlation is performed between the three reference PSS signals and the received signal. Since the location or GSCN of the received PSS is unknown and SS bandwidth is small compared to total transmission bandwidth, direct cross-correlation between the received signal and reference PSS is computationally complex and time-consuming. Furthermore, generating separate PSS reference signals for each of the 340 different GSCN needs huge on-chip memory. To address these challenges, we propose a novel architecture for PSS search as shown in Figure 7. In the proposed architecture, instead of generating reference PSS for every GSCN, we generate three reference PSS with the lowest GSCN, i.e., 7711. Thus, the received signal need to be down-converted to the lowest GSCN so that cross-correlation can be performed to detect the presence of the PSS. Since the GSCN of the received signal is not known, we need to perform down-conversion for all possible GSCN values and hence, a scheduler is used to generate appropriate down-conversion frequency, \(f_{GSCN}\), sequentially till the PSS is detected. Since PSS occupies only 7.2 MHz bandwidth, the size of correlators can be reduced by downsampling reference as well as received signals. We have achieved these operations via a digital down converter (DDC) which downconverts the received signal to the GSCN value selected by the scheduler followed by the low-pass filtering to remove aliasing and down-sampling by the chosen factor, \(D_{PSS}\). Since the downsampling results in a loss of signal and may affect the detection probability of the PSS, it should be chosen carefully based on the received signal-to-noise ratio (SNR). Empirically, higher SNR allows the use of larger \(D_{PSS}\) which in turn leads to faster PSS search and lower memory requirement. The final task after the PSS detection is to locate the starting sample of the OFDM symbol containing the PSS and generate an appropriate tick signal for further synchronization. This is done using the location of the correlation peak for the detected PSS and corresponding GSCN value.