esa-sceva/satcom-chunk-collection huggingface dataset:
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 4 The Taxonomy of Malware Analysis and Detection Approaches
### Malware Analysis Approaches and Data Types
#### 4.1.3 Hybrid Analysis  
Some previous studies combined data extracted through static and dynamic analysis together to reduce the drawbacks of both analysis approaches and achieve a higher detection rate. Different tools, including Cuckoo sandbox, IDA pro disassembler, and OlleyDbg, are employed to collect dynamic and static data, and then hybrid feature sets are created based on several types of data, such as string, opcode, API calls, and others [55; 56; 57; 58; 59]. Even though the hybrid analysis approach benefits from the advantages of both static and dynamic analysis, it also suffers from their disadvantages.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 7 Simulation--Real Topology
### Results--Real Topology  
The aim of the simulation was to find the number and positions of repeaters for a particular throughput level for communication between transfer station (HeadEnd modem) and Smart Meters.  
The topology between transfer station (n1) and Smart Meters (n32) was described in a previous chapter and the distance was 836 m.  
Table 1 shows throughputs for each route section from n1 to n32 according to the defined methodology. From these results it is obvious that the throughput on the physical layer of 1 Mbps can be achieved with six repeaters.  
According to simulation results shown in the Table 1, the 6 repeaters are necessary to overcome the bottleneck throughput of 1.68 Mbps in the route section n17 \(\rightarrow\) n18.  
The bottlenecks are route sections n17 \(\rightarrow\) n18 and n27 \(\rightarrow\) n28. These bottlenecks are caused by the overhead power line AIFe \(4\times 50\) and the transition between underground and overhead power lines.  
The cable type is not the only one reason for the bottleneck in the route section n17 \(\rightarrow\) n18. The branches (taps) in nodes n18 and n19 also cause the throughput decrease (see Section 6.4 Influence of number of branches).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measuring Point/** & **Distance [m]** & **Cable Type** & **Throughput on** \\
**Route Section** & & & **Physical Layer [Mbps]** \\ \hline n1 \(\rightarrow\) n17 & 231 (102 + 75 + 54) & NAYY 150 SE & 15.4 \\ \hline n17 \(\rightarrow\) n18 & 65 & ALFE \(4\times 50\) & 1.68 \\ \hline n18 \(\rightarrow\) n21 & 212 (43 + 169) & ALFE \(4\times 50\); AYKY \(3\times 120\) + 70 & 4.9 \\ \hline n21 \(\rightarrow\) n27 & 197 & NAYY 150 SE; AYKY \(4\times 50\) & 82.6 \\ \hline n27 \(\rightarrow\) n28 & 88 & ALFE \(4\times 50\) & 2.2 \\ \hline n28 \(\rightarrow\) n30 & 22 & ALFE \(4\times 50\) & 6.2 \\ \hline n30 \(\rightarrow\) n32 & 21 & ALFE \(4\times 50\) & 6.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Possibility of communication—without repeaters.  
The other decrease of throughput is caused by the technical solution of the repeating signal, because the BB-PLC modems and repeaters use the same medium, and so their transmissions have to be multiplexed. This issue was not yet considered in simulations, but considering the worst-case noise scenario, this decrease could be neglected.
# Defense and Security Mechanisms in the Internet of Things: A Review
## 6 Discussion
With increasing devices and data, systems must maintain high performance and low latency. Cybercriminals are constantly developing new attack techniques, leading to new patterns and types of anomalies. Detection algorithms must be flexible enough to recognise new attacks (zero-day) and adapt to changing threats. The main challenge is to find a balance between minimising the number of false alarms and high-threat detection. False positives may result in reduced operational efficiency and increased incident management costs. Such systems also have to deal with the issue of available resources. Resource-constrained systems, like IoT devices, often have limited computing capabilities. Therefore, detection methods must  
Figure 3: Accuracy of proposed defense methods (horizontal bars) based on [145; 150; 151; 152; 154; 153; 154]. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **F1-Score [\%]** & **Training Time [s]** \\ \hline RF & 99.2 & 98.5 & 97.8 & 98.1 & 120 \\ CNN & 97.8 & 96.3 & 95.5 & 95.9 & 320 \\ GNN & 96.5 & 95.0 & 94.2 & 94.6 & 290 \\ Ensemble Learning & 98.9 & 98.7 & 98.3 & 98.5 & 200 \\ LSTM & 95.5 & 94.0 & 93.8 & 93.9 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of intrusion detection methods on different datasets. be energy-efficient, lightweight, and resource-optimised models. Internal attacks also hold significant importance. Typically, traditional IDSs and IPSs concentrate on detecting external attacks. Detecting anomalies related to internal threats (for example, employee actions) is problematic because this activity can resemble typical behaviour. IoT networks and distributed systems experience various anomalies, from attacks to unexpected system failures. Detecting all anomalies with a single technique is challenging because each type requires specific characteristics and data processing techniques. Detecting anomalies in large systems like IoT systems requires scalable algorithms for real-time data. As devices and data grow, systems must maintain high performance and low latency. Detection algorithms must be flexible to recognise new attacks and adapt to changing threats. Balancing false alarms with high-threat detection is crucial, as false positives can reduce operational efficiency and incident management costs. Detecting internal threats is also challenging due to specific characteristics and data processing techniques. We also decided to present a detailed comparison of different security solutions for IoT environments, focusing on their applications, advantages, and limitations. This comparison is presented in Table 10. Each solution offers unique benefits in different IoT contexts but has limitations. Introducing such solutions requires understanding the trade-offs between computational requirements, implementation costs, and attack resistance. The presented comparison highlights the need for further research into solutions that can be more universal, scalable, and adapted to the constraints of IoT environments.
## 4 Experimental Investigation
### Numerical Simulation  
To verify the effectiveness of the proposed algorithm, the following system is considered:  
\[\left\{\begin{array}{l}x_{11}(k+1)=x_{21}(k)\\ x_{12}(k+1)=x_{22}(k)\\ x_{21}(k+1)=\dfrac{0.38x_{11}(k)}{1+x_{21}^{2}(k)}+2.2u_{1}(k)+h(x(k),u(k))\\ x_{22}(k+1)=(0.12+0.05\cos(x_{12}(k)))\cdot x_{22}(k)+2u_{2}(k)\\ y_{1}(k)=x_{11}(k)\\ y_{2}(k)=x_{12}(k)\end{array}\right. \tag{43}\]  
The desired signal is selected as follows:  
\[\left\{\begin{array}{l}y_{1d}(k)=0.25\sin(\dfrac{0.52k\pi}{50}+\frac{\pi}{4} )\\ y_{2d}(k)=0.21\cos(\dfrac{0.48k\pi}{50}-\frac{\pi}{4})\end{array}\right. \tag{44}\]  
and  
\[h(x(k),u(k))=0.1x_{11}(k)\sin(x_{21}(k))\cos(x_{22}(k)) \tag{45}\]  
The initial values of states are given as  
\[\left\{\begin{array}{l}x_{11}(k)=1.2\\ x_{12}(k)=0.5\\ x_{21}(k)=0.2\\ x_{22}(k)=0.31\end{array}\right. \tag{46}\]  
The tracking performances of \(y_{1}\) and \(y_{2}\) using MTN controller are shown in Figure 14.  
Figure 12: Error curve of MTN controller.  
Figure 13: Error curve of MTN controller.  
Figure 14a,b show the actual and the expected tracking curves of \(y_{1}\) and \(y_{2}\), where \(y_{1d}\) and \(y_{2d}\) are expected signals of \(y_{1}\) and \(y_{2}\), respectively. The tracking error between \(y_{1}\) and \(y_{1d}\) approaches zero, indicating that a high-quality tracking performance is achieved.  
To further demonstrate the superior performance of the proposed method, we compared it with the radial basis function (RBF)-based control method. Figure 15 shows the control results obtained using the RBF-based control method.  
The RBF robust controller exhibits a significant overshoot. Additionally, during periods of significant fluctuations in the reference signal, there is a noticeable output lag.  
Figure 14: Tracking performances of \(y_{1}\) and \(y_{2}\) with MTN. (**a**) Tracking performances of \(y_{1}\) (**b**) Tracking performances of \(y_{2}\).  
Figure 15: Tracking performance of \(y_{1}\) and \(y_{2}\) with RBF robust controller. (**a**) Tracking performance of \(y_{1}\) (**b**) Tracking performance of \(y_{2}\).  
In conclusion, the robust adaptive control method based on MTN proposed in this paper can effectively control such special unknown disturbance. The results of the above cases show that the control method proposed in this paper can provide better tracking performance and antidisturbance ability than the traditional NNPID control method.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment
### Experimental Analysis of Multi-Robots Path Planning
#### 4.4.1 Collision Detection  
As shown in Figure 13a, each robot moves towards the target point from the initial position, and robots A and C detect each other in their own peripheral circles. In order to prevent collision, the method shown in collision type II is adopted. Meanwhile, the robots with a lower priority will actively avoid the one with a higher one.  
As shown in Figure 13b, robots A and B detect the existence of each other by using the external circle method. In this case, collision type III is required for conflict resolution and avoidance, so that the robots can pass smoothly.  
As shown in Figure 13c, robots A and B detect each other using external circles, which belongs to collision type I. The corresponding conflict resolution method needs to be used to let the one with higher priority pass first, and the one with lower priority can pass only when the external circle is larger than the security zone.  
Figure 12: Trends in the best individual fitness of the GA model.
## 3. Results and Discussion
### Volume-Free Optimal Equal Bale Dimensions Design
#### 3.3.1 Algorithm 3  
Assume film parameters \(b_{f}\), \(v_{f}\), \(\varepsilon_{lf}\), overlap ratio \(k_{f}\), Equation (20), and the number of global film layers \(p_{l}\) are given. Take minimal \(\delta_{min}\), \(k_{fb,min}\) and maximal \(\delta_{max}\), \(k_{fb,max}\) values of the overlaps \(\delta\), \(k_{fb}\).  
1. Determine the set \(\mathcal{P}_{3}\) of all integer \(p_{b}\) defined by the inequalities from Equation (39) for which there exists \(2\leq q\leq 5\) satisfying applicability condition, Equation (21).
2. If the set \(\mathcal{P}_{3}\) is empty, then the solution to Problem 3 does not exist--go to step 3. Otherwise, go to step 4.
3. Change the lower \(k_{fb,min}\), \(\delta_{min}\) or upper \(k_{fb,max}\), \(\delta_{max}\) bounds of wrapping parameters or the film width \(b_{f}\) and go to step 1.
4. For any \(p_{b}\in\mathcal{P}_{3}\) compute the overlap ratio \(k_{fb}\) and, next, overlap \(\delta\) according to Equation (40).
5. For any \(p_{b}\in\mathcal{P}_{3}\) and assumed bale volume \(V_{b0}\) compute film usage obtained for the optimal \(D_{b}^{*}\), \(H_{b}^{*}\), Equation (19), using Equation (A2) and choose that \(p_{b}\) which yields the minimal film consumption.
# Analysis of the Current State of Automation of Hazard Detection Processes in BIM in Slovakia
## 4 Discussion
### Implications to Practice and Contribution to Knowledge  
The results from this survey changed the approach to solving hazard detection in BIM. Past approaches, presented in the Introduction, solved the automation of hazard detection but did not verify if the software, in which the plug-in was developed, was used by the construction companies or by the people who are responsible for HS. Software such as Autodesk Revit, Trimble Tekla or Bentley Synchro PRO are huge applications with hundreds of functions, often not very user-friendly and requiring long daily use experience, which is not very suitable for positions such as HS inspectors and project managers.  
A significant and inseparable part of the topic of hazard detection in BIM is also the transfer of information about the hazards to the person responsible. Detection can be performed by people in other positions (e.g., BIM coordinator), but the results have to be transferred to others (e.g., HS inspector). That is possible by different methods (video simulation, schedule, BCF), but what is the most effective way? How long does it take to transfer a report from a program and how long does it take to take action, such as installing a barrier, to avoid the hazard? This survey covered these issues, but the results from the interviews are currently being analyzed and will be published later.  
The data showed that researchers and software developers should concentrate more on solutions based on an open BIM approach and using available software for BIM coordination or CDE systems, which are exceedingly popular. Based on these results, a method for HS hazard detection and transferring information to the person responsible is in development and will be published later.
## 3 Results and Discussion
### Temperature Indices
#### 3.1.2 Number of Hot Days (Tmax > 30 \({}^{\circ}\)C)  
For the RCP4.5 scenario, the number of hot days in the north of Poland is projected to increase by 30% to 40% (1.9 days) in the 2030 horizon. (Figure 3). In the central part of the country, the projected increase is 20% to 30% (2.8 days), and in the southern part--10% to 20% (2.14 days). In the 2050 horizon, the number of hot days in most cities is expected to increase by 20% to 30% (3.0 days). Moreover, in some cities in the north and the south of the country, an increase of 30% to 40% (1.9 days) is expected.  
In the RCP8.5 scenario, in the 2030 horizon, the projected changes in the value are slightly lower than in the RCP4.5 scenario. In this scenario, an increase from 5% to 10% (1.2 days) compared to 2010 is dominant. Also, there are individual cases of cities in the southwest and northeast where the rate of increase is higher, ranging from 10% to 20% (1.79 days). In the RCP8.5 scenario for the 2050 horizon, a significant increase higher than 30% (4.4 days) in the number of hot days is projected over the entire country.  
Figure 2: Spatial distribution of the percentage change in the average annual temperature for 49 cities based on the results of statistical downscaling of the EURO-CORDEX model ensemble. (**a**) Scenario RCP 4.5 2030 (**b**) Scenario RCP 8.5 2030 (**c**) Scenario RCP 4.5 2050 (**d**) Scenario RCP 8.5 2050.
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Data Sources
#### 3.1.1 TROPOMI Data  
TRPOSpheric Monitoring Instrument (TROPOMI) [13] is a UV-Visible-Near-Infrared-Short-Wave Infrared (UV-Vis-NIR-SWIR) spectrometer on board the Copernicus Sentinel 5 Precursor (S5P) satellite. The satellite was launched in October 2017 and entered its operational phase in May 2018. It is a Sun-synchronous satellite with a local equatorial overpass time at 13:30. In 24 h, the satellite performs approximately 14 orbits and, with these, covers the full globe.  
The TROPOMI spectrometer measures spectra of several trace gases including nitrogen dioxide (NO\({}_{2}\)). Since NO\({}_{2}\) gas is the most notable product of photochemical reactions of NO\({}_{x}\) emitted by ships, it can be utilized for the ships' emission monitoring. The maximal ground pixel resolution of the TROPOMI instrument is equal to \(3.5\times 5.5\) km\({}^{2}\) at nadir. Due to the projection of the satellite images, the real size of the pixel will vary, depending on the true distance between the satellite and the part of the surface of the Earth being imaged. To generate images of regular size, we regridded (For the data regridding, the HARP v.1.13 Python package was used) the original TROPOMI data into a regular-sized grid of size \(0.045^{\circ}\times 0.045^{\circ}\), which for the pixel in the middle of the analyzed area translates to approximately \(4.2\times 5\) km\({}^{2}\).  
The following filtering criteria were applied to the TROPOMI data: \(qa_value>0.5\), \(cloud\)\(fraction<0.5\). Previous studies [11; 12] showed that such a selection of filtering values can yield considerably good results for a given task. The detailed description of the variables can be found in [31].  
In this study, 68 days from the period between 1 April 2019 and 31 December 2019 of TROPOMI measurements were analyzed. The analyzed days were mostly sunny--the distribution of the variable _cloud fraction_ for the scope of this study is provided in Figure 1. The studied data product was the tropospheric vertical column of nitrogen dioxide [32], data version: 1.3.0. For the analysis, an area in the Mediterranean Sea, restricted by the Northern coasts of Libya and Egypt from the south and the south coast of Crete from the north (lon: \([19.5^{\circ};29.5^{\circ}]\), lat: \([31.5^{\circ};34.2^{\circ}]\)) was chosen. This particular region was selected because of the presence of a busy shipping lane connecting Europe and Asia, the high frequency of occurrence of sunny days, and relatively low levels of NO\({}_{2}\) background concentrations, which are favorable conditions for the analysis. An outline of the area studied is presented in Figure 2.  
Figure 1: Distribution of the variable _cloud fraction_ for the dataset used in this study.
## 3 Results
### Antioxidant Effect of the EGEO  
The antioxidant effect was determined by DPPH radical scavenging and metal ion chelating activity (Table 2). The capacity of the EGEO to scavenge DPPH radical and its reducing ability was assessed on the basis of its dose giving 50% inhibition (IC\({}_{50}\)). In addition, its ability to chelate Fe\({}^{2+}\) metal ions was evaluated and calculated using different doses. The radical scavenging activities of positive controls (BHA and ascorbic acid) were more active than those obtained from EGEO. However, EGEO had a superior metal ion chelating activity with an IC\({}_{50}\) value of 8.43 \(\pm\) 0.03 mg/mL, followed by BHA (104.73 \(\pm\) 7.30 mg/mL) and gallic acid (136.97 \(\pm\) 9.09 mg/mL). Current results show that EGEO has the best chelating activity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Retention Time (RT, min)** & **RI §** & **Name \#** & **Concentration (\%)** \\ \hline
8.777 & 925 & \(\alpha\)-Pinene & 2.93 \(\pm\) 0.1 \\
9.685 & 969 & \(\beta\)-Pinene & 0.20 \(\pm\) 0.02 \\
10.110 & 983 & Myrcene & 0.19 \(\pm\) 0.07 \\
10.282 & 1000 & \(\alpha\)-Pellandrene & 0.59 \(\pm\) 0.02 \\
10.886 & 1033 & Eucalypt (1,8-Cineole) & 94.03 \(\pm\) 0.23 \\
11.282 & 1053 & \(\gamma\)-Terpinene & 1.93 \(\pm\) 0.17 \\ \hline \hline \multicolumn{4}{c}{_Oxygenated monoterenes_} & 94.03 \\ \hline \hline \multicolumn{4}{c}{_Monoterpene hydrocarbons_} & 05.86 \\ \hline \hline \end{tabular}  
* Compounds listed in order of elution from a non-polar column. § RI (retention index) calculated relative to C6–C17 n-alkanes.  
\end{table}
Table 1: Chemical profile of EO from _Eucalyptus globulus_ analyzed with GC-MS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **DPPH Radical Scavenging** & **Chelation IC\({}_{50}\)** \\
**IC\({}_{50}\) (mg/mL)** & **(mg/mL)** \\ \hline \hline \multicolumn{2}{c}{_Eucalyptus globulus_ Essential Oil} & 2.48 \(\pm\) 2.24 C & 8.43 \(\pm\) 0.03 A \\ Positive Control (BHA) & 0.13 \(\pm\) 0.05 B & 104.73 \(\pm\) 7.30 B \\ Positive Control (Ascorbic acid) & 0.018 \(\pm\) 0.004 A & 140.99 \(\pm\) 3.13 C \\ Positive Control (Gallic Acid) & — & 136.97 \(\pm\) 9.09 C \\ \hline \hline \end{tabular}  
* IC\({}_{50}\) = medium inhibitory concentration; values are given as mean \(\pm\) SD (n = 3). Means within the same column followed by the same capital letters are significantly not different (\(p>0.05\)) from one another according to the ANOVA test followed by [PERSON]’s comparison tests.  
\end{table}
Table 2: Antioxidant property of _Eucalyptus globulus_ volatile oil _in vitro_.
## 4 Methodology
### Correlation between Two Candidate Feature Vectors
#### 4.5.3 Correlation Functions Used in This Study  
Numerous statistical functions are available for assessing the dependency between variables. We specifically concentrate on one widely recognized method. [PERSON]'s rank-order correlation coefficient emerged as our primary focus. This coefficient effectively gauges the strength of a monotonic association relationship between vectors [36].  
**[PERSON]'s rank-order correlation coefficient:** This coefficient is a non-parametric (distribution-free), rank-based version of the Pearson coefficient that is utilized to measure the statistical dependency among the ranks of two vectors, and it measures the direction of the associated relationship among two vectors. It is most useful when the data distribution of the vectors has a linear association and does not require the measurement of these vectors at a certain interval scale because it is convenient for ordinal scale vectors, which means that the data should be transformed to ranks before this coefficient function is calculated [36]. It is defined as follows:  
\[s=\frac{\sum_{i=1}^{n}((\text{rank}(x_{i})-\text{rank}(X))(\text{rank}(y_{i})- \text{rank}(\Upsilon)))}{\sqrt{\sum_{i=1}^{n}(\text{rank}(x_{i})-\text{rank}( X))^{2}\sum_{i=1}^{n}(\text{rank}(y_{i})-\text{rank}(\Upsilon))^{2}}} \tag{12}\]where \(rank(x_{i})\) and \(rank(y_{i})\) are the ranks of the observation in the sample, and \(ran\tilde{k}(X)\) and \(rank(Y)\) are the mean rank of the variable \(X\) and the variable \(Y\) in the sample, respectively. The value of [PERSON]'s coefficient, \(s\), lies in \([-1,1]\) such that, when \(s\) moves towards 0, this is a weaker monotonic relationship between \(X\) and \(Y\).  
**Kendall's Correlation Coefficient**: Here, we discuss another correlation function that does not depend on assuming that the features have a monotonic relationship, such as [PERSON]'s correlation function. This coefficient measures the relationship among ordinal vectors, and it is defined as follows:  
\[k=\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}\text{sgn}(x_{i}-x_{j})\text{sgn}(y_{i}-y_ {j})}{n(n-1)} \tag{13}\]  
where  
\[\text{sgn}(x)=\begin{cases}1&\text{if }(x_{i}-x_{j})>0\\ 0&\text{if }(x_{i}-x_{j})=0;\text{sgn}(y)=\begin{cases}1&\text{if }(y_{i}-y_{j})>0\\ 0&\text{if }(y_{i}-y_{j})=0\\ -1&\text{if }(y_{i}-y_{j})<0\end{cases} \tag{14}\]  
This coefficient depicts the inconsistency degree among the number of concordant and discordant pairs. Any two pairs of ranks \((x_{i}-x_{j})\) and \((y_{i}-y_{j})\) are concordant if \(x-i<x_{j}\) and \(y_{i}<y_{j}\) and \(y_{i}>y_{j}\) or if \((x_{i}-x_{j})(y_{i}-y_{j})>0\). Conversely, if those conditions are reversed, the two pairs of ranks are said to be discordant. And the value of \(k\) lies in \([-1,1]\), which indicates the strength of the associated relationship among two vectors [123].