esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Project Overview and In-Situ Monitoring
### Structure of the Tunnel  
The bench method was used to excavate the super-span transition section. The tunnel was divided into 11 drifts for drilling and blasting and the numbers 1-12 represented the construction stages of it. Figure 5 has shown the support pattern and the excavation process of Section 5-5 of the super-span transition section in the rock of Grade V; detailed support parameters have been shown in Table 4. It should be noted that the shotcrete was sprayed in three layers using different formulations. The initial layer of the shotcrete was immediately sprayed with a thickness of 5 cm after blasting excavation to provide a safe working environment for subsequent construction. Therefore, C30 nano-silica and steel fiber reinforced concrete with high early strength was selected to be used. The 18.2 cm thick second layer of shotcrete construction was carried out after the steel arch had been installed; C30 steel fiber reinforced concrete with improved mechanical properties and toughness was selected to improve the load bearing capacity of the shotcrete. The third layer of shotcrete was sprayed after the prestressed anchor bolts and cables had been installed and tensioned to strengthen the primary supports; the layer was 11.8 cm thick, and the material used was C30 ordinary concrete. The temporary supports installed on the sidewalls of Drift 1 were removed during excavation of Drifts 2 and 3. In order to facilitate the following analysis, Drift 1, Drift 2 and Drift 3, Drift 4 and Drift 5, Drift 6 and Drift 7, Drift 8 and Drift 9, and Drift 10 were defined as the top heading (TH), the upper bench (UB), the middle bench (MB), the lower bench (LB), the rock pillar (RP), and the inverted arch (IA), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Size Weight** & **Elastic Modulus** & **Poisson’s Ratio** & **Cohesion (MPa)** & **Friction Angle** \\  & **(kN/m3)** & **(GPa)** & **Poisson’s Ratio** & **(MPa)** & **(°)** \\ \hline \hline
\begin{tabular}{c} Tuff breccia \\ Granite \\ Monzonitic granite \\ \end{tabular} & 27.0 & 11.3 & 0.30 & 0.8 & 40.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The typical physical and mechanical properties of the surrounding rock.  
Figure 4: Longitudinal geological profile of the project area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Grade I** & **Grade II** & **Grade III** & **Grade IV** & **Grade V** \\
**Value** & **(Very Good)** & **(Good)** & **(Fair)** & **(Poor)** & **(Very Poor)** \\ \hline \hline
\begin{tabular}{c} BQ \\ Q \\ \end{tabular} & \textgreater{}550 & 451–550 & 351–450 & 251–350 & \textless{}250 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relationship between the basic quality (BQ) system and the quality (Q) System.
## 6 Measured Instrument Parameters and Capabilities  
A number of important parameters of the NMR spectrometer were also evaluated, as part of the above measurement process:  
* _System signal-to-noise_: The controlling Graphical User Interface (GUI) configures the NMR data capture to start before the first NMR pulse, and this signal provides both a data base-line and uncontaminated signal that the instrument uses to measure the base noise-level, for each trace. A typical signal at the demodulated receiver output is shown in Figure 4. Measured noise levels appear to be around 1 mV RMS (for a typical 0.5 V signal) so a Signal-to-Noise (S/N) of about 500 for a typical 25 mg water sample in the 3 mm probe), for a signal filter setting of 250 kHz and 5 point integral filter (suitable for a typical NMR Free Induction Decay (FID) and Echo experiment with a liquid)--thus, an RMS noise level, single shot, equivalent to about 50 \(\upmu\)g for water. When conducting NMR Cryoporometric experiments, a slow warming ramp is needed so that all the sample is as isothermal as possible, and thus averaging times of 100 to 300 s per captured point were commonly employed. The NMR echo is fitted with a polynomial, and the polynomial solved to obtain a peak amplitude. This gave RMS noise levels for the averaged NMR Cryoporometric signal of around 50 to 100 \(\upmu\)V, so an S/N of about 5000 to 10,000 for typical 3 mm samples containing about 25 mg of water. This gives a very useful dynamic range to the resultant pore-size distributions, see Figure 9, the data for which appears to indicate the RMS sensitivity in a typical NMRC measurement to be around 15 nl of porosity. This is particularly important for low porosity samples.
* _NMR RF Pulse Power_: For this spectrometer, the linear RF transmitter amplifier delivers a \(\pi/2\) pulse into the supplied room-temperature NMR probe (5 mm OD standard NMR tube) of about 6 \(\upmu\)s, while, for the NMR Cryoporometric probe with a 3 mm OD NMR sample, it is about 9 \(\upmu\)s.
* _Recovery time after the NMR Pulse_: The time from the middle of the RF pulse to the first usable signal is a key feature for spectrometers when measuring more rigid materials such as solid polymers, waxes, tars, etc. With the receiver set to full bandwidth and 2 point integration (to suppress filter ringing), the system appears to recover pleasingly quickly, even for the Cryoporometric probe, with an overall time from the centre of the NMR pulse being no greater than 7 or 8 \(\upmu\)s. This enabled the measurement of FIDs from materials as rigid as bulk ice, see Section 4.2.
* _Portability_: A significant feature of this Field Programmable Gate Array (FPGA) based equipment is its compactness, which might make it suitable for use in the field or even for mobile use. As such applications are still to be evaluated, these measurements having been conducted on the laboratory bench-top. For mobile use, more compact non-mains power supplies will be needed.
# Fuzzy Clustering Approaches Based on Numerical Optimizations of Modified Objective Functions
## 2 Classical FCM and Modifications on Its Objective Function
### A Penalty for Large-Sized Clusters  
This modification of the objective function aims at keeping the generated clusters in comparable sizes to each other. It is involving an entropy term, which would stimulate more balanced membership degrees of data instances into the clusters:\[J(X,\ U,\ V;\ m,\ C,\ \lambda)=\sum_{i=1}^{n}\sum_{j=1}^{C}u_{ij}^{m}d^{2}\big{(}x_{ i},\ c_{j}\big{)}+\lambda\sum_{i=1}^{n}\left(\sum_{j=1}^{C}\mu_{ij}\right)^{2} \tag{5}\]  
The squared sum of the membership values acts efficaciously as a penalty for clusters growing large by accumulating a high sum of memberships. The idea of penalizing the large clusters has been employed multiple times, and it is comprised within the more general idea of clustering with constraints [20]. The squared sum of memberships is one of the approaches in this context aiming to ensure that the generated clusters will remain balanced.
# Methodological Framework to Collect, Process, Analyze and Visualize Cyber Threat Intelligence Data
## 2 Threat Intelligence Background
### Threat Hunting  
Threat Hunting (TH) is strongly linked to the concept of Advanced Persistent Threats (APT) because when Indicators of Compromise of a given APT are detected within organizational equipment and networks, security analysts must search and analyze logs from hosts, routers, servers and any other network-connected hardware or software to find if any IOCs entities appears on their logs, showing if the threat has penetrated the network or any hardware/software. This is primarily the definition of threat hunting--finding and discovering threats from IOCs and any other indicators that correlate tracks and footprints to a vulnerability or attacker.  
Normally, threat hunting represents a big challenge for those who need to confront it. It has four main challenges [13,14]: The first one is about searching at scale, i.e., because of the attack pattern of attackers; it is necessary to link related IOCs together even if the attacker's footprints exist in a long period of time--such as in weeks or months. Hence, TH must be able to deal with long-time-scattered data and still be able to identify threats and vulnerabilities along its systems.  
The second challenge is about identification and correlation of threat entities, i.e., the ability to look the entire scenario of a threat campaign to fully identify all the steps of the attacker, avoiding being cheated by the attacker if he tries to masquerade his footprints.  
The third challenge is about the confidence score of threat generated data. These data should have as few false positives as possible so that cyber response operations can be performed smoothly and without rework.  
Finally, the last TH challenge is how to obtain a complete picture of a threat when analyzing only fragmented pieces of data such as IP addresses, hashes, domain names, process names or any other useful information. Weak threat-hunting data results in weak analysis, as well as a poor response to any threat that may benefit from that specific vulnerability.  
When all these TH challenges are overcome, an organization will have a large set of data--structured, semi-structured and natural language data--composed of text files, images, documents and any other possible kind of data that will be ingested and processed by a CTI tool or a security professional to deliver intelligence and finally to work on threats and vulnerabilities.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## Appendix A  
Let \(\mathbf{A}\) be a matrix with conjugate symmetry by definition [29]:  
\[\mathbf{A}=\begin{bmatrix}1&1&1&1\\ 1&i&-1&-i\\ 1&-1&1&-1\\ 1&-i&-1&i\end{bmatrix}, \tag{10}\]  
and \(\mathbf{x}\) a complex vector with equidistant zeros:  
\[\mathbf{x}^{T}=\begin{bmatrix}x_{0}&0&x_{1}&0\end{bmatrix}. \tag{11}\]  
Performing the multiplication of matrix \(\mathbf{A}\) by vector \(\mathbf{x}\), the following is obtained:  
\[\mathbf{y}=\begin{bmatrix}1&1&1&1\\ 1&i&-1&-i\\ 1&-1&1&-1\\ 1&-i&-1&i\end{bmatrix}\begin{bmatrix}x_{0}\\ 0\\ x_{2}\\ 0\end{bmatrix}. \tag{12}\]  
Simplifying the multiplication, we obtain:  
\[\mathbf{y}=\begin{bmatrix}x_{0}+x_{1}\\ x_{0}-x_{1}\\ x_{0}+x_{1}\\ x_{0}-x_{1}\end{bmatrix}, \tag{13}\]  
the resulting vector \(\mathbf{y}\) contains repeated values due to the presence of equidistant zeros in vector \(\mathbf{x}\) and the conjugate symmetry of matrix \(\mathbf{A}\). The number of repeated values will be equal to the number of equidistant zeros between different nonzero values.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 3 Experiments
### Experiments Settings and Metrics  
The robustness of AGLNM, CGMP, FC-LSTM, SVR, CFCC-LSTM, and GED were compared by using the open-source deep learning tool PyTorch and the open-source machine learning tool LibSVM on SST data sets of the Bohai Sea and South China Sea, respectively. The six models were trained and tested on the daily mean SST data set to predict the future SST of 1, 3, and 7 days for each spatial point. Specifically, Adam was selected as the optimization algorithm of the model and the learning rate was initialized to 0.01 with a decay. The batch size of the input data was set to 128 and the number of iterations was set to 1000. The mean square error (MSE) and mean absolute error (MAE) were selected as the evaluation indexes of prediction performance. The smaller the MSE or MAE, the better its performance. MSE, MAE, and experimental losses are defined as follows:  
\[\text{MSE}\big{(}Y,Y^{\prime}\big{)}=\frac{1}{l}\sum_{i=0}^{l}\big{(}y_{i}-{y_{ i}}^{\prime}\big{)}^{2} \tag{12}\]  
\[\text{MAE}\big{(}Y,Y^{\prime}\big{)}=\frac{1}{l}\sum_{i=0}^{l}\big{|}y_{i}-{y_{ i}}^{\prime}\big{|} \tag{13}\]  
\[L_{AGLNM}=L_{Pre}\big{(}Y-Y^{\prime}\big{)}+L_{GL} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sea Area** & **Spatial Coverages** & **Purpose** & **Temporal Coverages** \\ \hline Bohai Sea & & train & 1 January 2008 to 26 November 2012 \\ (1/4\({}^{\circ}\) latitude/1/4\({}^{\circ}\) longitude) & & test & 27 November 2012 to 31 December 2013 \\ \hline South China Sea & & 6.5 N to 21.5 N, & train & 1 January 2008 to 26 November 2012 \\ (1/4\({}^{\circ}\) latitude/1/4\({}^{\circ}\) longitude) & & 112.5 E to 119.5 E & test & 27 January 2012 to 31 December 2013 \\ \hline \end{tabular}
\end{table}
Table 1: The statistics of data sets.  
where \(Y\) is the predicted future SST series, \(Y^{\prime}\) is the corresponding observed SST series, \(y_{i}\) is the SST series contained in the prediction window, \(y_{i}\)\({}^{\prime}\) is the observed SST series corresponding to the window, and l is the total predicted length. In Formula (14), the model adopts the sum of the predicted loss \(L_{Pre}(Y-Y^{\prime})\) and graph loss \(L_{GL}\) as the combined loss to participate in the training. The data-driven training model loss can simultaneously learn the association relation of the SST sequence and the graph structure most suitable for the current data set.
# Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales
## 2 Modeling of Distributed Power System
### Modelling of Distributed Energy Carriers
#### 2.2.3 Distributed Energy Storage System  
There are two main types of energy storage devices: the power-type storage devices and the energy-type storage devices. Compared with the energy-type storage devices, the power-type storage devices have a faster response time and a longer lifespan [30; 31]. They are widely used in renewable energy generation systems to smooth the output power of wind turbine and PV systems. The super-capacitor, one of the most mature power-type storage devices, is extensively used to stabilize power system fluctuation [32], whose maximum output power (\(P_{sc\max}\)) can be expressed as:  
\[P_{sc\max}\ =V_{sc}\times i_{sc\max} \tag{10}\]  
\(V_{sc}\) is the terminal voltage of the super-capacitor, and \(i_{sc\max}\) means the maximum output current of the super-capacitor. In order to achieve its maximum output power, the capacity of the super-capacitor (\(Q_{sc}\)) should meet the following requirement:  
\[Q_{sc}=\frac{i}{dV}\times(dt+\tau) \tag{11}\]  
where:  
\[i=\frac{i_{sc\max}\ +i_{sc\min}}{2} \tag{12}\]  
In Equations (11) and (12), \(i\) represents the average current, \(dV\) is the difference between the maximum operation voltage and the minimum operation voltage of the super-capacitor system, \(dt\) is the time of discharge, \(\tau\) means the time constant, and \(i_{sc\min}\) refers to the minimum output current of the super-capacitor.  
Unlike the power-type storage devices, the energy-type storage devices have the advantages of longer storage time and larger energy storage capacity. Lead-acid batteries, as the most mature energy-type storage devices, are widely applied to shave the peak demand in a power system [33], whose maximum output (\(P_{b\max}\)) can be expressed as:  
\[P_{b\max}=V_{b}\times i_{b\max} \tag{13}\]  
where \(V_{b}\) is the terminal voltage of the battery system, and \(i_{b\max}\) means the maximum output current of the battery. In order to improve battery lifespan, the maximum output current of the battery is limited by the installation capacity of the battery. Therefore, the initial capacity of the battery (\(Q_{b}\)) can be described as [33]:  
\[Q_{b}=\beta\times i_{b\max} \tag{14}\]  
where \(\beta\) represents the maximum discharge current coefficient of the battery.  
Since the distributed hybrid energy storage system (super-capacitor and battery) does not consume fossil energy and exhaust carbon dioxide, the carbon emissions of a hybrid energy storage system are ignored in this paper.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 2 Proposed Charger/Discharger
### Switched Model  
The mathematical model of the charger/discharger is obtained by studying the differential equations governing the converter for the two possible conditions of the control signal \(u\), which are reported as follows:  
* When \(u=1\) (\(a=0\)) Mosfet M\({}_{1}\) is ON and Mosfet M\({}_{2}\) is OFF, which imposes the following differential equations on the magnetizing inductance current \(i_{Lm}\) and bus voltage \(v_{dc}\): \[\frac{d\;i_{m}}{dt}=\frac{v_{b}}{L_{m}}\] (1) \[\frac{d\;v_{dc}}{dt}=-\frac{i_{dc}}{C_{dc}}\] (2)
* When \(u=0\) (\(a=1\)) Mosfet M\({}_{1}\) is OFF and Mosfet M\({}_{2}\) is ON. For this analysis the effect of both the leakage \(L_{k}\) inductance and bus voltage is calculated at the primary side of the HFT, while the effect of the magnetizing inductance is calculated at the secondary side of the HFT: \[\frac{d\;i_{m}}{dt}=-\frac{v_{dc}/n}{L_{m}+L_{k}/n^{2}}\] (3) \[\frac{d\;v_{dc}}{dt}=\frac{(i_{m}/n)-i_{dc}}{C_{dc}}\] (4)  
Combining the previous differential equations leads to the following switched model:  
\[\frac{d\;i_{m}}{dt}=\frac{v_{b}\cdot u}{L_{m}}-\frac{v_{dc}\cdot(1-u)}{n\cdot L _{q}} \tag{5}\] \[L_{q}=L_{m}+\frac{L_{k}}{n^{2}}\] (6) \[\frac{d\;v_{dc}}{dt}=\frac{1}{C_{dc}}\cdot\left[\frac{i_{m}\cdot( 1-u)}{n}-i_{dc}\right] \tag{7}\]  
Moreover, the leakage inductor current \(i_{k}\), and the currents \(i_{p}\) and \(i_{s}\) presented in Figure 2, are:  
\[i_{k}=i_{s}=\frac{i_{m}}{n}\cdot(1-u) \tag{8}\] \[i_{p}=i_{m}\cdot u \tag{9}\]
## 5 Discussion
Research findings indicate that natural geologic hazards, such as rockfalls, are influenced by a variety of factors, including the natural environment and human activities. Considering the natural environment, rainfall, temperature, sunshine, vegetation, etc., can have an impact on the slope, potentially leading to rockfall accidents. Human activities have an impact on the entire ecosystem, resulting in the overall destruction of slopes, reduced vegetation cover, and more frequent rockfall accidents. In the process of future urban development and construction, ecological protection should be the guiding principle to minimize the damage to the stability of the original ecosystem and to achieve a harmonious coexistence between human beings and nature. Land development and utilization should try to avoid areas with high ecological vulnerability and a high risk of rockfall disasters. Necessary ecological restoration can also be appropriately implemented in areas with a high risk of geohazards to restore and rebuild the stability of regional ecosystems. Figure 19: Rank of highway rockfall risk. There are some limitations in this study; first of all, in the selection of evaluation indexes. The selection of indexes of rockfall susceptibility can also consider the influence of climatic factors such as the terrain humidity index and distance from rivers, etc. The terrain humidity index is a physical indicator of the influence of regional topography on runoff flow and accumulation, and the larger the terrain humidity index is, the closer the distance from rivers is, which will result in the increase in water content and softening of slopes, leading to the reduction in slope stability, thus leading to the occurrence of rockfall accidents [PERSON] et al., 2024 [61]. Therefore, these are also important indexes to consider in the evaluation of rockfall susceptibility elements, and the influence of these indicators can be further considered. The evaluation indexes of rockfall vulnerability elements can additionally consider transportation elements, such as highway traffic flow and other indexes, which can further reflect the size of the impact on the highway when rockfall accidents occur. Furthermore, the 12.5 m DEM data still has certain limitations. A higher-precision DEM can identify rockfall accidents in a smaller range more accurately because the higher the precision of the DEM, the more accurately it can reflect the real terrain conditions, and the occurrence of rockfall accidents can also be judged more accurately. In addition, the accuracy of the evaluation model established in this study can be further improved. It was found that the AUC value of this model reached 0.827 through the ROC curve, but now there are quite a number of research models of the ROC curve of the AUC value that can reach 0.85. It can be considered to select more rockfall accident disaster points, as more data will improve the accuracy of the model described in this research.
# Predictive Quantization and Symbolic Dynamics
## 5 Transfer Entropy Estimation
### Mutual Information Neural Estimation  
Theoretically, computing the mutual information between two variables is a hard problem. The computation is only tractable if two variables are discrete or if two variables' probability distributions are known. For the latent variables sampled from a VAE, we cannot directly compute the mutual information since it is intractable to marginalize the probabilities from the whole latent space.  
Mutual information neural estimation (MINE) [20] is a framework that allows us to use a neural network output to approximate the mutual information between two variables. Suppose that we want to estimate the mutual information between \(X\) and \(C\), we first construct the joint data points \((x_{1},c_{1}),(x_{2},c_{2}),....,(x_{N},c_{N})\), where for each \(n\), the tuple \((x_{n},c_{n})\) hypothesizes that these two variables are related. Then, we create a new group of data points \((x_{1},c_{a_{1}}),(x_{2},c_{a_{2}}),....,(x_{N},c_{a_{N}})\), where \((a_{1},a_{2},...,a_{N})\) is a randomly shuffled index sequence from 1 to \(N\). Next, we feed these two groups of data points into a neural network \(T_{\theta}\) to converge by a loss function:  
\[L(\theta)=\frac{1}{N}\sum_{n=1}^{N}T_{\theta}(x_{n},c_{n})-log(\frac{1}{N}\sum _{n=1}^{N}e^{T_{\theta}(x_{n},c_{a_{n}})}) \tag{24}\]  
where minimizing this loss is able to find a tighter lower bound that can approximate the true mutual information using the Donsker-Varadhan representation of the KL divergence. Generally, the mutual information estimation is performed by finding a mapping between two sets of data points, so it is a \"point-wise\" mutual information that is not easily applicable to time series or predictive information. In the following experiments, we will use MINE to analyze the mutual information between the latent variables of the generative model data \(X\) versus the context \(C\).