esa-sceva/satcom-chunk-collection huggingface dataset:
# Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet
## 3 Setup for Numerical Simulation
### Evaluation Criteria and Variable Selection  
The groove and tiny cracks created by the high-pressure water jet will increase the degree of freedom for rock breaking, which can reduce the forces and wear of the disc cutter and increase the rock breaking efficiency. This paper chooses disc cutter kinetic energy and rock crushing volume as the evaluation criteria to judge the performance of the water jet. In certain times, a smaller decrease in disc cutter kinetic energy represents smaller forces and wear of the disc cutter, while more rock crushing volume represents a higher rock breaking efficiency.  
The velocity, diameter, and location of the water jet were chosen as the variables for obtaining the optimum parameters of the rock broken by the disc cutter coupled with the high-pressure water jet. The location of water jet is the distance between the center of the water jet and the center section of the disc cutter, which is shown by \(S\) in Figure 4. The details of the three variables are shown in Table 3.  
The jet velocity and jet diameter are related to the energy of the water jet for rock breaking, which can be expressed as Equation (4). The jet velocity is chosen as the only variable to determine the energy of the water jet for rock breaking. The energy of the water jet for rock breaking per unit time must be constant when the jet diameter changes. As shown in Equation (4), the jet velocity is inversely  
Figure 4: Location of the water jet.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Jet velocity (m/s) & 100/141/173/200/224/245 \\ Jet diameter (mm) & 1/1.5/2/2.5/3 \\ Jet location (mm) & 0/10/12/14/16/18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values chosen for the variables.  
Figure 3: Numerical model of a rock failure impacted by abrasive slurry jet (ASJ).  
proportional to the jet diameter. Therefore, the jet velocities (200/153/126/109/96 m/s) correspond to the jet diameters (1/1.5/2/2.5/3 mm), respectively.  
\[E_{t}=\frac{1}{2}\rho_{w}QV^{2}=\frac{\pi}{8}\rho_{w}D^{2}V^{3} \tag{4}\]  
where \(E_{t}\) is the energy of the water jet for rock breaking per unit time, \(\rho_{w}\) is the density of the water jet, \(Q\) is the volume flow rate of the water jet, \(V\) is the jet velocity, and \(D\) is the jet diameter.  
Parameters about the disc cutter were fixed as a constant in the numerical simulation. The initial linear velocity of the disc cutter is 52.3 mm/s, the initial angular velocity of the disc cutter is 0.24 rad/s, and the penetration of the disc cutter is 8 mm.
## 2 Materials and Methods
### Analysis of the Dataset
The KS test values, representing the maximum absolute differences between the cumulative distribution functions, were calculated for the features C, Mn, Si, Cr, Ni, Mo, Cu, Jominy distance, and hardness (HRC). The KS test values obtained are between 0.020 and 0.033, indicating relatively small differences between the distributions. In addition, \(p\)-values were calculated for each test, ranging from 0.37 to 0.91. Significantly, all calculated \(p\)-values were relatively high, indicating no substantial evidence to reject the null hypothesis of similarity between the distributions. These results provide confidence in the similarity of the feature distributions between the training and test datasets and confirm the robustness of our partitioning of the dataset for training and evaluating the models, i.e., the model is likely to generalize well to the unseen test dataset, see Figure 2. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Statistics**} & \multirow{2}{*}{**Datasets**} & \multicolumn{8}{c}{**Predictors**} \\ \cline{3-10}  & & **C** & **Mn** & **Si** & **Cr** & **Ni** & **Mo** & **Cu** &
\begin{tabular}{c} **Jominy Distance** \\ \end{tabular} \\ \hline \multirow{2}{*}{Maximum} & Training & 0.7 & 1.4 & 0.41 & 1.92 & 2.739 & 0.43 & 0.34 & 50 \\ \cline{2-10}  & Test & 0.7 & 1.4 & 0.41 & 1.92 & 2.739 & 0.43 & 0.34 & 50 \\ \hline \multirow{2}{*}{Minimum} & Training & 0.12 & 0.36 & 0.12 & 0.09 & 0.04 & 0.0098 & 0.07 & 1.5 \\ \cline{2-10}  & Test & 0.12 & 0.36 & 0.12 & 0.09 & 0.04 & 0.0098 & 0.07 & 1.5 \\ \hline \multirow{2}{*}{Average} & Training & 0.28 & 0.83 & 0.26 & 1.00 & 0.47 & 0.12 & 0.17 & 17.54 \\ \cline{2-10}  & Test & 0.28 & 0.82 & 0.26 & 1.01 & 0.50 & 0.12 & 0.16 & 18.28 \\ \hline \multirow{2}{*}{Standard Deviation} & Training & 0.12 & 0.26 & 0.04 & 0.33 & 0.58 & 0.09 & 0.05 & 14.18 \\ \cline{2-10}  & Test & 0.13 & 0.26 & 0.04 & 0.34 & 0.60 & 0.09 & 0.05 & 14.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics of the training and test datasets used in modeling the hardenability of steels. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Features** & **C** & **Mn** & **Si** & **Cr** & **Ni** & **Mo** & **Cu** &
\begin{tabular}{c} **Jominy Distance** \\ \end{tabular} & **Hardness** \\ \hline KS test value & 0.026 & 0.025 & 0.027 & 0.026 & 0.022 & 0.027 & 0.020 & 0.033 & 0.023 \\ \hline \(p\)-value & 0.65 & 0.70 & 0.62 & 0.67 & 0.82 & 0.60 & 0.91 & 0.37 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Kolmogorov–Smirnov test results of goodness of fit of the training and test datasets.
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 4 Experiments
### Experimental Prerequisites  
* A TCP/IP dataset containing normal traffic and attacks (Section 3). This dataset is available as raw captures (_pcaps_) and already preprocessed in a CSV file according to the UNSW-NB15 Argus/Bro format (Section 2.1).
* A Ground Truth (GT) file with information linking attacks and flows in the dataset, provided together with the UNSW-NB15 dataset (Section 3).
* Five sets of feature vectors to represent traffic (introduced in Section 2).
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 5 Discussion
However, the LCOE values were calculated without taking into account the incentives (SuperBonus 110% or 50% tax deduction, preferential VAT, on-site exchange, -i.e., the sale of energy) thanks to which the installation would be worthwhile even with a return of energy prices to pre-2020 values. Areas for the improvement of our estimate mainly concern the quality of the data available for analysis. The data used in the work were not particularly recent: the building blueprints, despite having been digitally published in 2017, were obtained from regional databases compiled between 1994 and 2000, and partially updated in 2006, while the digital surface model was based on LiDAR observations performed between 2008 and 2013. Therefore, there is a discrepancy between the data shown by the raster and the cadastral data (e.g., the raster of 2013 might show a building not yet registered in the blueprint dataset); the difference between the current situation (2022) and the one described by the data might not be negligible (entire neighbourhoods might be built in a decade). In terms of data accuracy, the digital surface model does not account for the presence of empty spaces; sometimes the building blueprints also include internal courtyards within the perimeter (e.g., see Figure 14); situations such as these are usually discarded due to the presence of vegetation that makes the presumed building coverage irregular. The available data do not yet cover the entire territory of Sardinia or even the entire set of buildings; the overall estimate was obtained by rescaling the calculation made on the covered area, but it is evident that a complete coverage would lead to fewer uncertainties.
## 2 Materials and Methods  
### Extraction Procedure: Technical and Operative Details  
CTs were produced at the laboratory of Basilicata University (Italy). The extraction process lasted 48 h and was realized in an extracting system by assembling the following components (Scheme 1): 50-L plastic containers (a); jute bags; a 24 L compressor (b); a 5 L compressor (c); a solenoid valve supplied with 24 V (d); 15 m of \(\mathcal{O}\) 16 mm irrigation tube with T-shaped end (e); 20 m of \(\mathcal{O}\) 6 mm micro-irrigation tube; a digital timer (220 V). The \(\mathcal{O}\) 16- and 6-mm aeration tubes were pierced to obtain high air pressure to assure mixing and oxygenation of the extracting liquid during the process. The 24-L compressor was connected with the solenoid valve and provided air flow to the largest diameter tubes (turbulence effect) (Scheme 1f). The planned turning on of the solenoid valve by the digital timer allowed air injection for 5 min every three hours. The 5-L compressor was connected to an air distributor (Scheme 1g) made up of a 16-mm pipe, on which 6-mm pipes were connected using adapters. During the extraction process the 5-L compressor provided air to the smallest diameter tubes which, placed in the container according to a spiral arrangement, generated a \"sparkling\" effect (Scheme 1h). Each jute bag with filter function was filled with 7 L of compost, sealed at the top and immersed in a container filled with additives and dechlorinated tap water. Dechlorination (removal of chlorine used to disinfect tap water) was carried out by bubbling water for at least 20 min. This caused the chlorine to be released as a gas.  
**Scheme 1.** Technical and operative details of the CT extraction process. **(a)** 50 L-plastic containers; **(b)** 24 L compressor; **(c)** 5 L compressor; **(d)** solenoid valve supplied with 24 V; **(e)** T-shaped end; **(f)** turbulence effect; **(g)** air-distributor made up of 6-mm pipes connected to a main 16-mm pipe by adapters; **(h)** spiral arrangement of the smaller tubes (\(\O\) 6 mm) in the container and sparkling effect.
## 5 DRLAttack in the Black-Box Setting
### Improvement of Reward Function  
In the white-box setting, the design of the reward function primarily depends on the hit rate (HR@K) of the target item in the recommendation lists of target users, because attackers can directly access the output of the recommendation system and accurately assess the promotion effect of the target item. However, when transitioning to a black-box setting, attackers face the limitation of not being able to directly access all users' recommendation lists, and can only inject a small number of spy users to obtain recommendation lists, which significantly reduces the applicability of the original reward function design. To address this, we propose an improved reward function design that combines the positional information of the target item in the recommendation list with the hit rate, expanding the range of reward values and thereby enhancing the learning efficiency of the agent and the optimization effect of the attack strategy. Considering the significant impact of the target item's position in the recommendation lists of spy users on the effectiveness of the attack, the improved reward function is as follows:  
\[\begin{array}{l}R(s_{t},a_{t})=\sum\limits_{i=1}^{|U_{spy}|}(HR+(k-Pos(i)))\\ HR(u_{i*}^{A},v_{*})=\left\{\begin{array}{ll}1,&v_{*}\in L_{u^{*}}^{A}\\ 0,&v_{*}\
otin L_{u^{*}}^{A}\end{array}\right.\end{array} \tag{13}\]  
where \(HR\) represents the hit rate of the target item in the recommendation list of spy users, \(k\) is the length of the recommendation list, \(Pos(i)\) represents the position of the target item in the recommendation list of the \(i\)-th user. If the target item is not in the list, then \(Pos(i)=k\), meaning it is considered to be outside the list. \(k-Pos(i)\) represents a weighting factor, using the reciprocal of the position as the weight. The specific solution process can be seen in Algorithm 1.  
```
Input: Spy user's recommended list \(l_{u_{spy}}\), length of recommended list \(k\), target item \(I\) Output: Position information \(Pos(i)\)
1: Initialize an empty list \(pos=[]\)
2: initialization parameter \(index=0\)
3:\(pos\gets l_{u_{spy}}\)
4:for\(index\) in \(k\)do
5:if\(pos[index]=I\)then
6:\(Pos(i)=index\)
7:else
8:\(index=index+1\)
9:endif
10:endfor
11:return\(Pos(i)\)
```  
**Algorithm 1** Solving for Position Information \(Pos(i)\)
## 2 Experimental Section
### Photoelectrochemical Characterization  
Photoelectrochemical performances of SnO\({}_{2}\)/WO\({}_{3}\)/BiVO\({}_{4}\) photoanodes were measured with a typical three electrode configuration, using an Ivium potentiostat with Pt plate as a counter electrode and Ag/AgCl as a reference electrode. A 0.5 M Na\({}_{2}\)SO\({}_{3}\) with potassium phosphate buffer solution was used as the electrolyte for all the measurements. The light intensity of a solar simulator with an AM 1.5 G filter was calibrated to 1 sun (100 mW/cm\({}^{2}\)), using a reference cell. Linear sweep voltammetry (LSV)measurements were carried out by sweeping in the anodic direction with a scan rate of 20 mV/S. The incident photon to current conversion efficiency (IPCE) values were measured at 1.23 V vs. Reversible hydrogen electrode (RHE) using light source with monochromator. Electron impedance spectroscopic (EIS) measurement was conducted at 1.23 V vs. RHE with a frequency range of 10 mHz-1000 Hz in aqueous 0.5 M Na\({}_{2}\)SO\({}_{3}\) with phosphate buffer.
# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## 2 Conventional Approaches
### Supervised Methods
#### 2.2.3 Support Vector Machine (SVM)  
Considering the data organized into a matrix, each subject corresponding to a row vector can be conceived as a single point in the p-space of the considered variables. Data can be essentially organized into two main groups, \"separated by a gap\" whose margins are defined by support vectors. Instead, the edge located in the gap center separating the data corresponds to the dividing hyperplane. SVM tries to define the support vectors, and the prediction will indicate to which hyperplane side the new observations belong. However, generally, data cannot be linearly separated, and hence it is difficult to determine the separating hyperplane. Nevertheless, SVM can accurately execute a non-linear classification throughout the so-called kernel trick. It consists of an implicit mapping of the considered inputs into high-dimensional feature spaces with the objective to their linear separation in that space [68]. In detail, the optimal hyperplane is the one that provides the highest separation between the two classes. With greater definition, by separation, we mean the maximum amplitude (or width, w) between the lines parallel to the hyperplane without any data points in between. This optimal hyperplane is called the maximum-margin hyperplane and the corresponding linear classifier is the maximum-margin classifier (Figure 9).  
Figure 8: **(a)** Bidimensional PLS-DA score plot of urine samples obtained from different hospitals. HB—Basurto Hospital, CRC—Cruces Hospital, HD—Donosti Hospital, TX—Txagorritxu Hospital. Figure reprinted from [67] under the terms of Creative Commons Attribution 4.0 International License. **(b)** OPLS scheme.  
In addition, in the presence of mislabeled data, the SVM can provide inadequate classifications; therefore, only a few misclassified subjects are found instead by maximizing the separation between the two classes. Finally, validation methods and diagnostic measures are analogous to those adopted in PLS methods. Ultimately, SVM is one of the approaches with the highest accurate prediction, since it is based on statistical learning frameworks [69; 70]. It can also be used within machine learning approaches for anomaly detection (such as weather) by choosing an anomaly threshold with the aim to establish whether an observation belongs to the \"normal\" class or not. Disadvantages of supervised methods include overfitting problems [71] corresponding to the inclusion of noise inside the statistical model. These issues can be provoked by excessive learning, so several validation techniques, such as cross validation [72] or bootstrapping [73], are usually employed to solve them.
## 4 Simulation and Results
### Complexity Analysis  
The following analysis provides a comprehensive examination of the complexity associated with each element of this protocol (Figure 11), based on the provided parameters and their respective weights. Each element, including Active Reader, Collision, Delay, and Overhead, is dissected to understand how various factors contribute to the overall complexity of the protocol. By examining the dependence of parameters such as the number of readers and frequency, time slots, and canal ranges, this analysis aims to shed light on the intricacies of the protocol, enabling a deeper understanding and potential avenues for optimization. Let us delve into the detailed analysis of each element:  
To create a complexity formula for each element based on its parameters, we use a weighted sum approach, where each parameter is multiplied by its corresponding weight. Let us designate the parameters as follows:  
R: Number of readers;  
T: Number of tags;  
F: Number of frequency;  
S: Number of time slots;  
D: Range of data channel;  
C: Range of control channel.  
The complexity formulas are as follows:  
1. Active Reader (A) \[A=0.4R+0.2T+0.7F+0.6S+0.8D+0.9C\]  
The complexity of active reader management in the DTS-AC protocol is heavily influenced by the allocation of frequency and time resources, with significant weight assigned to these parameters. Additionally, the number of readers and the range of control channels play important roles in determining the overall complexity.  
Figure 11: The complexity of the DTS-AC protocol. 1—number of readers, 2—number of readers, 3—number of frequency, 4—number of time slots, 5—read range, 6—control range.  
2. Collision (Cn) \[Cn=0.4R+0.08T+0.6F+0.6S+0.5D+1C\]  
The collision management complexity is primarily driven by the number of readers and the range of control channels, which are assigned the highest weights. The allocation of frequencies and time slots also contributes significantly to the overall complexity of collision resolution.  
3. Delay (DI) \[Dl=0.7R+0.2T+0.8F+0.9S+0.9D+0.6C\]  
Delay analysis complexity is predominantly influenced by the number of readers and the range of data channels, both of which carry substantial weight. Additionally, the allocation of frequencies and time slots plays a significant role in determining the delay management complexity.  
4. Overhead (O) \[O=0.94R+0T+0.6F+0.6S+0.56D+0.9C\]  
The overhead complexity is primarily determined by the number of readers, which carries the highest weight. The range of control channels and the allocation of frequencies also contribute significantly to the overall overhead management complexity.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 2 Intrinsic Height Errors and Location-Induced Errors
### Location-Induced Errors
#### 2.2.2 Layover  
For a layover situation, the radar echo hits the top (\(B1\) and \(B3\)) of the terrain before the bottom (\(B2\)). Therefore, \(B1\) and \(B3\) are recorded first in the image plane and then \(B2\), which is the reverse of the ordering on the ground. The echo includes signals from the top and bottom as well as fore-slope and/or back-slope which are projected to the radar image plane (\(B^{\prime}\)). As the signals are out of order and mixed together, it is ambiguous and makes target detection challenging [48]. Similar to foreshortening, the layover pattern in radar images leans toward the sensor, but it creates brighter features (i.e., higher intensity backscatter) within the images.  
The geometric condition of layover is \(\alpha>\theta\). In other words, once the local terrain slope exceeds the incidence angle, foreshortening turns into layover. Consequently, as the incidence angle rises, it will be more difficult for layover to occur. In general, if the local terrain slope is over \(40^{\circ}\), it is highly likely that layover will occur for both ERS-1 and Sentinel-1 SAR platforms. Both the local terrain slope and the incidence angle need to be very steep to observe the layover effect.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 4 Experimental Setup
### Key Generation and Security Measures  
To establish secure communication, ECC keys for both the server and client were generated utilizing a script before authentication begins. These keys were securely stored in designated files in respective nodes and loaded during runtime.  
The experimental setup helps us to evaluate the protocol's capability to deliver secure, scalable, and efficient communication within a smart grid system.
## 2 Methods
### CUDA Implementation of Greedy Agglomerative Procedures
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm or**} & \multicolumn{4}{c}{**Achieved Objective Function Values (1) Summarized after 30 Runs**} \\ \cline{2-5}  & **Min (Record)** & **Max (Worst)** & **Average** & **Median** & **Std. Dev** \\ \cline{2-5}  & **Min (Record)** & **Max (Worst)** & **Average** & **Median** & **Std. Dev** \\ \hline Lloyd (multistart) & 99.7279 & 123.1325 & 110.4963 & 109.9706 & 5.6551 \\ j-means (SWAP\({}_{1}\) + Lloyd) & 48.3311 & 57.1689 & 51.6673 & 51.1146 & 2.0749 \\ AGGL\({}_{1}\) & 51.9696 & 81.9848 & 64.2454 & 62.4350 & 8.3403 \\ AGGL\({}_{2}\) & 44.4866 & 50.0924 & 46.1544 & 46.0973 & 1.0708 \\ AGGL\({}_{3}\) & 43.8615 & 46.3881 & 45.1735 & 45.3303 & 0.7306 \\ AGGL\({}_{5}\) & 43.7373 & 46.0294 & 44.8369 & 44.7245 & 0.6888 \\ AGGL\({}_{7}\) & 44.0820 & 48.4483 & 45.2256 & 45.0851 & 0.9135 \\ AGGL\({}_{10}\) & 43.8603 & 45.7149 & 44.6455 & 44.6387 & 0.5400 \\ AGGL\({}_{15}\) & 43.7995 & 45.5927 & 44.7005 & 44.7173 & 0.5203 \\ AGGL\({}_{20}\) & 43.6629 & 46.0316 & 44.4626 & 44.3353 & 0.5953 \\ AGGL\({}_{25}\) & 43.6357 & 46.5550 & 44.5179 & 44.3408 & 0.6543 \\ AGGL\({}_{30}\) & 43.6728 & 44.5830 & 44.1677 & 44.1137 & 0.2945 \\ AGGL\({}_{50}\) & 43.6202 & 45.0433 & 44.2257 & 44.3137 & 0.3590 \\ AGGL\({}_{75}\) & 43.6375 & 45.6856 & 44.1450 & 43.9860 & 0.4683 \\ AGGL\({}_{100}\) & 43.6054 & 44.9035 & 44.0472 & 43.9875 & 0.3061 \\ GH-VNS1 & 47.7171 & 59.6970 & 53.4896 & 53.1948 & 3.4123 \\ GH-VNS2 & 43.7781 & 46.0085 & 44.8602 & 44.9149 & 0.5772 \\ GH-VNS3 & 43.8585 & 46.4490 & 44.7263 & 44.6593 & 0.6018 \\ SWAP\({}_{1}\) (the best of SWAP) & 47.8800 & 52.4030 & 49.8287 & 49.5523 & 1.1239 \\ GA-1 POINT & 61.2677 & 76.9114 & 67.2297 & 66.9182 & 3.8341 \\ GA-UNIFORM & 68.6276 & 102.0867 & 84.8705 & 83.8152 & 7.4129 \\ AggI-EA & 43.5826 & 44.4452 & 43.7486 & 43.7560 & 0.1624 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative results for Mopsi-Joensuu dataset. 6014 data vectors in \(\mathbb{R}^{2}\), \(p=100\) centers, time limitation 5 s.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 6 Reception Results  
This section will present the data received from satellites in orbit using the developed ground station. Each satellite has various missions depending on the installed payload and sensors, transmitting different types of data such as high-resolution images, low-resolution images, and scientific reports. Some data are available for general users to decode and use. Each type of data is transmitted in different frequencies and bandwidths. In this research, the ground station was used to receive signals from each satellite, and the data that can be received and decoded will be presented.