esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Learning Stack for APT Detection
## 2 Current Detection Problems  
In our previous paper [1], we proposed a novel method for detecting (APT) attacks by using OODA loop and Black Swan theory. The purpose of the method is to take into account current detection problems and improve detection rate for earlier unknown attacks, which is currently quite low. In our method, the intention is not to manipulate network data flow before it is passed to the detection process. That is, instead of, for example, reducing dimensions or standardising input data, we pass the data directly to the detection process. This way, one does not reduce overall complexity or randomness of the data.  
Current anomaly detection research [4, 5] relies on similar methods, where outdated and heavily manipulated datasets, such as KDD98, KDDCUP99 and NSLKDD [6], are used for benchmarking proposed methods. Another issue is that complex problems are solved in a simple manner, that is, one type of method is used for handling the entire problem, such as one type or Machine Learning (ML) or Deep Learning (DL) algorithm. Additionally, [PERSON] [7] et al. pointed out in their research that current intrusion detection systems rely heavily on attack fingerprints, which are data sequences to identify an attack, or attack profiles, which are behaviour models of an attack. Both mentioned methods require prior knowledge of an attack, thus cannot be used for detecting earlier unknown attacks. The authors proposed a framework that identifies sequence of events, separates unexplained cases based on prior models and estimates the probability of an attack. Moreover, [PERSON] [8] et al. stated that the number of smartphones is increasing and they are somewhat vulnerable, which increases the probability for being a victim of an APT. These devices are connected to cloud services all the time, increasing personal data transmission, as well as the probability for espionage. Considering mentioned issues, we can state that the overall complexity in a network increases due to growing number of devices and data transmissions, which also adds more randomness to data.  
[PERSON] [9] et al. stated in their study that the academic community has neglected research related to APT attacks, and, while research exists, it is too focused on detecting attacks afterwards. Therefore, their approach focused on preventing human error, which commonly is the reason that a campaign gets started in the first place. In addition, human error cannot be eliminated completely, thus more research on sophisticated detection methods are also required. As mentioned above, \"_...[APT attack] real-time detection might not be possible nor necessary. Instead, the focus is to drop the detection time from years or months to an acceptable one, that is, days_\" [1].
# Comparative Analysis for Slope Stability by Using Machine Learning Methods
###### Abstract  
Earth slopes' stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (FS) plays an essential part in the stability assessment, which guarantees operations' success. Providing accurate and reliable FS can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate FS calculations. The presented study focused on the FS estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate FS for the earth slopes. These machine learning classifiers have a strong capability predict the FS under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes' stabilities, recorded based on FS from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by [PERSON]'s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study's results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the FS than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the FS prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments.  
machine learning; slope stability; predictive models; limit equilibrium analysis; factor of safety +
Footnote †: journal: Applied Sciences  
0023
# Secure and Efficient Authentication Scheme in IoT Environments
## 4 Cryptoanalysis of the Proposed Protocol
### Formal Security Validation Using ProVerif Tool  
ProVerif is widely accepted as an automatic cryptographic protocol verifier tool in the symbolic model (the so-called Dolev-Yao model) [14]. This protocol verifier depends on the portrayal of the protocol by Horn clauses. In this subsection, we implemented the proposed scheme in the Proverif tool to test its correctness of mutual authentication. We performed this experiment on an Intel Core i5-8500 computer, with a 6-core 3.10 GHz CPU and a Windows 10 OS, provided by a service provider. This simulation used the ProVerif version 2.00 binary package [43] to stimulate the registration, mutual authentication, and session key agreement phase between an embedded device and a server. A description of the ProVerif simulation tool can be found in [14].  
As depicted in Figure 8, the model comprises three fragments, namely, parameter declaration, principal process, and query execution. The parameter-declaration fragment includes variables, names, and channels other than those with cryptographic capacities. The meanings of the principal process and subprocess are explained in the process segment, although the outline of the assessing scheme is set up in the query-execution segment. A couple of channels, constants, variables, and factors other than those with cryptographic capacities, outlined as constructors and conditions, are displayed in the parameter-declaration fragment. Furthermore, as shown in Figure 9, the principle-process fragment characterizes the commencement and end of the participating clients. The procedures of the execution of the participating clients are kept parallel. Toward the end of the execution, three queries are executed to amend the correctness and secrecy of the proposed scheme. The consequences of the queries are shown in Figure 10. The rightness of the proposed scheme is substantiated because the initial two queries are executed successfully, which indicate the successful beginning of initial interaction between the devices and the server, although its secrecy is affirmed because of an unsuccessful query (i.e., last query in the Queries section in Figure 10) attack on the session key.  
Figure 8: Declaration of parameters, with event and query execution.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 5 Cache Replication
### Two-Layer Mapping  
We will present the system model and problem formulation for RSU content replication in VANETs. Without loss of generality, we assume that the set of RSUs with uniform capacity are distributed all over the urban areas. All the file items are of unit size, and each RSU can replicate items at most. Therefore, we have a mapping between the replicas and scattered RSUs, which can be denoted as a replication layer mapping. Furthermore, considering the content delivery, we can denote the joint time period for each moving vehicle to download its requested item passing by neighbor RSUs. We also assume that the connection between any vehicle and RSU is uniform in the coverage area, and the bandwidth capacity of RSUs is usually not considered as the constraint in VANETs according to previous research [16; 22]. Therefore, the number of vehicles that can be served by a single RSU can be assumed to be unlimited, as these connections are transient for the moving traffic flow. Although there can be overlapped coverage areas between neighbor RSUs, we consider that the vehicles always connect to the nearest RSU. Generally, a vehicle needs to complete the content transmission through  
Figure 4: Ladder graph for bidirectional traffic flow.  
several RSUs passing by that are replicated the same content item. Therefore, we can further have a transmission layer mapping between the RSUs and vehicles.  
A two-layer mapping model is presented in Figure 5. The replication layer mapping indicates the content replication among different RSUs. The transmission layer mapping indicates the service connection between the vehicles and their passing-by RSUs. Given the two-layer mapping graph in Figure 5, we can consider the RSUs as the middle level connecting the replica nodes and the vehicle nodes. We can formulate the problem as a minimum replication vehicle cover problem, in which the request demand of vehicles should be supported with limited number of RSUs. This problem can be formulated in details as follows. We denote the set of replicas as \(R=\{r_{1},r_{2},...,r_{m}\}\), the set of RSUs as \(S=\{s_{1},s_{2},...,s_{k}\}\), and the set of vehicles as \(V=\{v_{1},v_{2},...,v_{n}\}\). Our object is to find a replication layer mapping \(f_{1}=\{1,...,m\}\rightarrow\{1,...,k\}\) and a transmission layer mapping \(f_{2}=\{1,...,k\}\rightarrow\{1,...,n\}\) with \(\sum_{i:f_{1}(i)=j}r_{i}\leq c\) for all \(j\in\{1,...,k\}\) and \(\sum_{j:f_{2}(j)=i}s_{j}\geq\sigma\) for all \(l\in\{1,2,...,n\}\) such that \(k\) is minimum. In the minimum replication vehicle cover problem, \(\sum_{i:f_{1}(i)=j}r_{i}\leq c\) for all \(j\in\{1,...,k\}\) represents the replication capacity limitation in the replication layer, and \(\sum_{j:f_{2}(j)=l}s_{j}\geq\sigma\) guarantees the service time for all the vehicles in the transmission layer. This problem can be correlated to the conventional minimum weight vertex cover problem, which has been well known as the NP problem.
## 5 Conclusions  
Concerns about climate change, future oil depletion and growing energy demand are fostering ways for advanced and efficient power systems. New paths are thus being investigated for next generation heat and power systems. Supercritical thermodynamic cycle systems including Rankine, Brayton and heat pumps have been reviewed from experimental and commercial viewpoints, focusing on their application for heat recovery. Several conclusions can be drawn from the study.  
First of all, supercritical operation is already well accepted in specific situations. These include supercritical Rankine cycles (e.g., classical steam power plant) and to a lesser extend also supercritical heat pumps and Brayton cycles working with CO\({}_{2}\). Although CO\({}_{2}\) appears as a promising fluid, a lot of research is still needed as it calls for new engineering design.  
Secondly, very few working fluids are considered suitable for supercritical operation. Either the critical temperature doesn't match the application or the working fluid is not stable at high pressure and temperature or there are other (i.e., environmental, safety, cost) constraints.  
Specifically for low-temperature transcritical cycles, experiments in literature are scarce and available results display poor performances mainly due to non-optimized turbomachinery. Supercritical Brayton cycles are intensely researched for its higher efficiency but experiments show we are still far from expected performances. Furthermore research is lacking in heat transfer behavior of supercritical fluids, especially for the refrigerants used in ORCs and heat pumps.  
Supercritical thermodynamic cycles have the potential to increase energy efficiency but at the moment high performing experimental test-rigs are missing to demonstrate this. In the future an integrated design is needed where all of the main components are optimized for supercritical operation (e.g., pump, expander, compressor, heat exchanger). The results of this could allow to increase the adoption rate of supercritical thermodynamic systems and to increase energy efficiency.  
Review on supercritical CO2 power cycles, B.T.; drafting of heat to upgraded heat Section--review, G.K.; draft of introduction and Section 4--review and final editing, S.L.; draft of introduction section, A.P.; review on steam Rankine cycles, supercritical ORCs and conclusions, E.N.; contribution to intermediate and final review, D.M.; final review, M.D.P.  
The work of G.K. relevant to the heat pumps has been conducted within the framework of the Industrial Scholarships program of the National Center for Scientific Research \"Demokritos\", co-funded by Stavros Niarchos Foundation. [PERSON] is a postdoctoral fellow of the Research Foundation-Flanders (FWO, 12T6818N). [PERSON] has received funding from the European Union's Horizon 2020 research and innovation program by the grant agreement No. 764042 (CHESTER project).  
The authors declare no conflict of interest.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
## 4 Results
### Force Exertion  
Figure 9 shows the results of the force exertion tests. The force exertion was high across most of the operating range, and especially so at low contractions, as expected when considering the shape of the actuator and direction of the tension force. To exert a tension of \(T\), the tension in each membrane of the chamber depends on \(1/cos(\theta)\), which tends towards infinity as \(\theta\) tends towards \(\pi/2\). Higher pressures are required at higher strains to sustain the required membrane tension, thus the force exertion at high strains is low as the pressure is held constant.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & \multicolumn{2}{c}{RMSE/mm} & \multicolumn{2}{c}{Error X/mm} & \multicolumn{2}{c}{Error Y/mm} & \multicolumn{2}{c}{Error Z/mm} \\ \cline{2-10}
**Path** & **X** & **Y** & **Z** & **max.** & **min.** & **max.** & **min.** & **max.** & **min.** \\ \hline Circle 10 mm \(\varnothing\) YZ & 1.4866 & 1.7571 & 1.5900 & 1.7720 & 1.2182 & 3.6386 & 0.1624 & 3.6629 & \(-\)0.4152 \\ Circle 10 mm \(\varnothing\) at 45\({}^{\circ}\) pitch & 0.7274 & 1.3102 & 1.4734 & 1.6933 & \(-\)0.8127 & 2.8806 & 0.1970 & 3.2618 & \(-\)0.2895 \\ Helix 20 mm \(\varnothing\) & 5.0991 & 1.7962 & 1.8811 & 3.8503 & \(-\)27.7232 & 5.0012 & \(-\)2.4748 & 4.7601 & \(-\)2.3074 \\ Conic spiral 30 mm \(\varnothing\) base & 2.3663 & 1.6742 & 2.1441 & 5.8209 & \(-\)1.0461 & 2.6694 & \(-\)4.5342 & 4.0849 & \(-\)4.5019 \\ Spiral on 20 mm \(\varnothing\) cylinder & 2.5468 & 2.5980 & 1.5421 & 4.2084 & 1.4722 & 3.1964 & \(-\)6.8052 & 4.5496 & \(-\)2.9175 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary table for results of each of the three-DOF tests.  
Figure 8: Results of path following tests for the three-DOF setup. (**A**) Circle 10 mm diameter. (**B**) Circle at 45\({}^{\circ}\) pitch. (**C**) Helix 20 mm diameter. (**D**) Conic spiral 30 mm base. (**E**) Spiral projected onto 20 mm diameter cylinder.  
Figure 9: Measured force exertion of an actuator blocked at given contraction values.
## 3 Results and Discussion
### Mechanical Properties  
#### 3.2.1 Hardness  
The hardness versus location plots across the through thickness direction for all cases are shown in Figure 8. The hardness profiles of all the experimental cases, including the bimetallic interface, are shown in Figure 8. The hardness of SS308L fell within the range of 139-188 HV. On the Inconel625 side, the hardness values ranged from 187 to 222 HV.  
In Case 1, where no pressure was applied, the measured hardness was correspondingly low. In Case 2, 10,000 N was applied to the Inconel section, but the effect seems to have been a slight improvement in hardness in the SS308L section. In the SS308L section, for each progression from Case 1,2 (non-pressure), to Case 2 (5000 N), and finally to Case 3 (10,000 N), there was a successive enhancement in hardness by 20 HV on average.  
For Inconel625, an average hardness increase of approximately 22 HV was observed in Cases 2, 3, and 4 (10,000 N) compared to Case 1 (non-pressure). Overall, a significant enhancement in the hardness of SS308L was observed as compared to Inconel625, likely attributed to the superior mechanical robustness inherent to Inconel625.  
The material was work-hardened due to a rise in dislocation density, which was a consequence of the increased rolling load that led to an enhancement in hardness [16].  
Figure 8: Hardness profile along the build direction of all experimental cases.  
Figure 7: SEM and EDS mapping of the laves phase (Case 4).
## 4 System Description
### Algorithm
Then, the drone waits at the station until the scheduled time for the next flight is reached. The functioning algorithm of the node is presented in Figure 7. After the setup has been completed, the node starts gathering the data from the nodes and storing it on an SD card. If it is the scheduled time for the drone flight, the node gets activated to wait for the drone. If it is not the scheduled time, the node goes into sleep mode for the time specified in the setup parameters. If a drone is detected, all the data stored on the SD card are forwarded to the drone. When the node receives a disconnect message, the node disconnects from the drone and deletes all the data stored in the SD card. Then, the node goes into sleep mode for the time stipulated on the settings so as to save energy and to avoid the drone having interferences with other nodes. While the node does not detect the drone, it checks if the maximum time activated time is reached to determine if it needs to go to sleep mode. This way, the node is not constantly activated if the drone decides to cancel the flight due to weather conditions. Then, it keeps gathering the data from the sensors each time the time interval between measures loaded from the setup has been reached. The message exchange between the elements of the architecture is presented in Figure 8. The drone sends beacons so the nodes can detect it and ask for a connection. When the connection establishment is performed, the node sends the data to the drone. The drone then sends an Acknowledgment (ACK) and a Disconnect message to indicate that the node should go into sleep mode for the stipulated time. After that, the node sends an ACK to the drone to indicate that the Disconnect message has been received correctly. The process is repeated with all the nodes deployed on the field within the coverage area of the drone as it follows its route. When the node reaches the base station, the data are forwarded to the database, and the database confirms the reception of the data with an ACK. Figure 6: Algorithm of the drone.
## 2 Materials and Methods
### Analytical Method
#### 2.3.2 Pharmaceuticals  
The target compounds in the collected samples were analyzed following a procedure based on solid phase extraction (SPE) and ultrahigh-performance liquid chromatography combined with tandem mass spectrometry (UHPLC-MS/MS) [23]. The analytical methods in the study were described by [PERSON] et al. [27], but we optimized the methods in our laboratory. The optimized SPE procedure used Oasis-HLB cartridges (200 mg/6 mL) conditioned with 6 mL of methanol followed by 6 mL of ultrapure water. A total of 1.0 L of seawater was percolated through the cartridge under vacuum conditions (5 psi). Afterwards, the cartridge was rinsed with 10 mL of ultrapure water and then dried under vacuum conditions for 0.5 h to remove the excess water. Elution was performed with 10 mL of methanol. Extracts were evaporated under a gentle stream of nitrogen at 40 \({}^{\circ}\)C and reconstituted with 200 \(\upmu\)L of acetonitrile-ultrapure water (30:70, v/v). Finally, 10 \(\upmu\)L of a caffeine-D3 standard solution was added to obtain a final concentration of 10 \(\upmu\)L in the extraction of the internal standard and analyzed by UHPLC-MS/MS.  
Instrumental analysis was performed with a UHPLC-MS/MS system consisting of an AB SCIEX Triple QuadTM 4500 triple-quadrupole mass spectrometer (AB SCIEX Corporation, USA) equipped with an electrospray ionization source and a Shimadzu Nexera UHPLC system (Shimadzu Corporation, Kyoto, Japan), consisting of two solvent delivery modules LC-30 AD, a column oven CTO-30 AC, and an autosampler SIL-30 AC. An Agilent Poroshell 120 EC-C18 column (4.6 mm\(\times\)150 mm, 2.7 \(\upmu\)m) (Agilent, USA) was used. Optimized separation conditions were achieved using ultrapure water (0.01% FA (v/v) as solvent A and acetonitrile (0.01% FA (v/v) as solvent B at a flow rate of 400 \(\upmu\)L/min. The gradient elution was performed as follows: Initial conditions, 5% B; 1-1.1 min, 5-15% B; 1.1-9.0 min, 15-75% B; 9.0-9.1 min, 75-95% B; 9.1-11.5 min, 95% B; 11.5-11.6 min, return to initial conditions; 11.6-15 min, equilibration of the column. The injection volume was 5 \(\upmu\)L, and the column oven was set at 40 \({}^{\circ}\)C. The autosampler was operated at 8 \({}^{\circ}\)C. Analyses were performed in the positive mode for all compounds. The source temperature was 350 \({}^{\circ}\)C. Nitrogen was employed as the nebulizer gas (30 psi) and the dry gas (10 L/min), and the interface voltage was set at 5.5 kV. A mass spectrometer was operated in multiple reaction monitoring (MRM) mode, and two MRM transitions were monitored for each compound, with the most intense transition used as a quantifier and the other used as a qualifier. The MRM transitions for each compound were optimized by direct injection of individual standard solutions of each compound at 1 mg/L. A summary of individual MS/MS parameters is presented in Table S1.
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 3 Evaluation of Strain Determination Using RBFs
### Real Application Example of a Tube under Tension and Torsion
#### 3.2.2 Rough Versus Smooth Surface Properties  
In the next study, the tensile problem for a displacement of the testing machine's traverse of 1.49 mm is looked at. Figure 13 (left) shows the maximum principal in-plane surface Green strains \(\epsilon_{\max}^{(2)}\) of the rough specimen and the results for the smooth outer  
Figure 11: Comparison of (**bottom**) triangulation result for torsion and (**top**) RBF (IMQ + Mon) showing principal maximum Green strain \(\epsilon_{\max}^{(2)}\) (the specimen’s region is projected onto the \((Y/Z)\)-plane and rotated by \(90^{\circ}\) to reduce the figure size).  
Figure 12: Maximum principal strain distribution \(\epsilon_{\max}^{(2)}\) using a facet size of 19 px with a point distance of 15 px (IMQ + Mon).  
surface specimen (right). The larger strains occur in the notches so that the strains obtained in the regions of the welding bead stacks are smaller than for the machined specimen with the smooth outer surface. This is clear since the regions of the welding bead stacks are the stiffest areas in the specimen.  
A similar behavior can be observed for torsion. Here, we took a torsional angle of \(\vartheta=4.32^{\circ}\), see Figure 14.  
Even here, the maximum principal strains \(\epsilon_{\text{max}}^{(2)}\) are within the transition of the welding bead stacks. Since this torsional angle is much smaller than in the previous study, it becomes obvious that the influence of the rough surface increases significantly at higher load levels.