esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
###### Abstract  
The massive modern technical revolution in electronics, cognitive computing, and sensing has provided critical infrastructure for the development of today's Internet of Things (IoT) for a wide range of applications. However, because endpoint devices' computing, storage, and communication capabilities are limited, IoT infrastructures are exposed to a wide range of cyber-attacks. As such, Darknet or blackholes (sinkholes) attacks are significant, and recent attack vectors that are launched against several IoT communication services. Since Darknet address space evolved as a reserved internet address space that is not contemplated to be used by legitimate hosts globally, any communication traffic is speculated to be unsolicited and distinctively deemed a probe, backscatter, or misconfiguration. Thus, in this paper, we develop, investigate, and evaluate the performance of machine-learning-based Darknet traffic detection systems (DTDS) in IoT networks. Mainly, we make use of six supervised machine-learning techniques, including bagging decision tree ensembles (BAG-DT), AdaBoost decision tree ensembles (ADA-DT), RUSBoosted decision tree ensembles (RUS-DT), optimizable decision tree (O-DT), optimizable k-nearest neighbor (O-KNN), and optimizable discriminant (O-DSC). We evaluate the implemented DTDS models on a recent and comprehensive dataset, known as the CIC-Darknet-2020 dataset, composed of contemporary actual IoT communication traffic involving four different classes that combine VPN and Tor traffic in a single dataset covering a wide range of captured cyber-attacks and hidden services provided by the Darknet. Our empirical performance analysis demonstrates that bagging ensemble techniques (BAG-DT) offer better accuracy and lower error rates than other implemented supervised learning techniques, scoring a 99.50% of classification accuracy with a low inferencing overhead of 9.09 \(\mu\) second. Finally, we also contrast our BAG-DT-DTDS with other existing DTDS models and demonstrate that our best results are improved by (1.9-27%) over the former state-of-the-art models.  
Keywords:cybersecurity; machine learning: Internet of Things (IoT); IDS system; networks; darknet; blackhole; ensemble learning +
Footnote †: journal: _Electronics_
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 3 Satellite Networks Vulnerability Risk Assessment Method  
In the previous chapter, we systematically analyzed the dimensions of the penetration testing method for satellite networks. However, vulnerability identification alone remains insufficient to evaluate the systemic security impacts. As a critical element of security management, vulnerability risk assessment serves an essential function in quantifyingthreat severity. This chapter proposes a vulnerability risk assessment method for satellite networks, exploring how a scientifically sound evaluation method can accurately identify high-risk vulnerabilities. This provides actionable strategies to fortify satellite network security defenses.
## 6 Case Study: Monitorization of a Sugar Cane Field
Now, we show a second example by considering eight sensors in the sugarcane field that define the variable \(PTS\) as follows:  
PTS := [[9, 43], [12, 3], [20, 30], [29, 18], [49, 5], [57, 38], [76, 52], [90, 39]]  
In Figure 11, we show the Voronoi diagram and weighted Delaunay graph associated with these nodes. Now, we run the routine eulerianGraph and obtain the output  
\"The graph is semi-Eulerian\"  
\"Semi-Eulerian Path:\" \([7,1,2,3,6,7,8,5,6,1,3,4,5,2,4,6,8]\)  
This means that our Delaunay graph has only two vertices with odd degrees. If we apply the algorithmic procedures based on Lemma 1, createHullEdges, findOtherEdges, countElements, and isK3, we obtain:  
\"Is Eulerian? :\"  
\"No, all the intern edges do not create K3\"  
Figure 12 shows the internal triangles. Figure 11: Voronoi diagram and weighted Delaunay graph associated with the second example. Figure 12: Internal triangles create K3. Next, we execute the algorithm hamiltonianGraph and we obtain:  
The graph is Hamiltonian  
The Hamiltonian cycle is \([1,2,3,4,5,6,8,7,1]\)  
The result is the order we should follow to visit every sensor once in a closed trail. Finally, we evaluate the two subroutines wheelCondition and chromaticNum. The outputs obtained are:  
\"The graph is not Eulerian\"  
\"The graph or a subgraph is a Wheel Graph\", \(W_{5}\)  
\"The wheel graph is odd\"  
\"The chromatic number must be 3\"  
\"The chromatic number is:\", 3  
\[[[2,6],[1,4,8],[3,5,7]]\]  
Consequently, the chromatic number of the Delaunay graph considered in this second example is 3 and we also know the color of each vertex. This allows us to minimize the tasks or roles assigned to the nodes. The first one would be assigned to nodes 2 and 6, the second one to vertices 1, 4, and 8, and the last one to 3, 5, and 7. It is also possible to avoid interference or save energy considering the previous assignment as we mentioned for the first example.
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Sensing Applications Based on Radiation Pressure
#### 4.2.1 Displacement Sensing
The sixth order mechanical mode was identified with a high frequency reaching up to 4.2 GHz  
Figure 16: Schematics of photonic crystal paddle nanocavity (inset: mechanical modes of paddle cavity) [117; 118; 119]. Figure 17: Schematics of optomechanical crystals for displacement sensing: (**a**) a central PCN cavity coupled to double-slotted photonic crystal slabs [120]; (**b**) sliced PCN cavity [47]. with the clamping limited mechanical Q-factors ranging from 240 to 1730. Electro-optomechanical systems have the merits of wide-range tuning, fast electrical control of optical resonances, and electrically tunable radiation pressure backaction effects. Such hybrid systems could be applied to achieve the signal conversion between optical and microwave frequency domains and are expected to exploit high-performance sensing applications for radiation pressure enforced displacement, mass and magnetometry measurements.
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
## 4 Results  
To evaluate the performance of the firmware update method proposed in this paper, five distinct firmware updates were carried out using the end device and application previously described. The updates performed varied in size regarding the number of records in the Intel Hex File and the number of rows to be updated in the microcontroller's program memory. The analysis of the proposed method's implementation was conducted separately in two distinct temporal domains. The first domain pertains to the time required for transmitting the Intel Hex File update records in the LoRaWAN network downlink, and the second domain refers to the actual update process time on the end device's microcontroller. Figure 20 presents the time diagram related to the update exemplified in the previous section, corresponding to Update #3 in Table 3, where the reception times of frames sent by the network server to the end device are shown. We can observe the reception, through Port 7, of the information related to the update to be carried out, which includes the name and the number of records to be received. Following this, the reception of different blocks of the update file is observed, with the transmission ending upon receiving the end of file block. The time associated with transmitting the firmware update information through the LoRaWAN network was 93.66 s.  
Figure 18: Timestamps of UART TX communication activities from RA-07H radio transceiver. Figure 19: Timestamps of UART RX communication activities from RA-07H radio transceiver. From the analysis of the reception times of the records, it was found that the transmission time of an update can be determined using Expression (2), with the value of 5.22 s corresponding to the average value of the reception window (RX_Delay) (Figure 20). Transmission time of update file  
\[\left[\mathrm{s}\right]=\mathrm{Intel}\mathrm{Hex}\mathrm{ File}\mathrm{ Number}\mathrm{ of}\mathrm{ Records}\times 5.22 \tag{2}\]  
From the results obtained from the firmware updates performed, it was concluded that the greatest time constraint is associated with the data transmission time over the LoRaWAN network, which depends on the size of the RX Delay window operating in Class A. The transmission time is thus dependent on the number of records to be updated, while the time spent on the update process in the microcontroller mainly depends on the number of rows to be updated (Table 3). To assess the times associated with the different tasks involved in the microcontroller update process, mechanisms were implemented to signal the start and end of each task by activating/deactivating IO pins of the microcontroller. Figure 21 shows the timeline diagram for executing the different stages of the proposed update process for Update #3. These stages include data integrity verification, preparation of new rows for update, copying rows for recovery scenarios, erasing old rows, inserting new data, and finally verifying the update process. The overall firmware update process is represented by the blue line in Figure 21, while the flash memory write operations are
## 5 Comparison with the Design Codes
### The Design Codes Description  
#### 5.1.1 Eurocode 4  
The plastic resistance of steel-concrete composite members subjected to axial compression can be estimated with increased core concrete strength caused by the confinement stresses achieved by the steel tube. The axial strength according to EC4 (\(N_{u,EC4}\)) of a CFST is determined as:  
\[N_{u,EC4}=\eta_{a}A_{s}f_{y}+A_{c,c}f_{c,c}\bigg{(}1+\eta_{c}\frac{t}{D}\frac{f _{y}}{f_{c,c}}\bigg{)}+A_{c,f}f_{c,f}\bigg{(}1+\eta_{c}\frac{t}{D}\frac{f_{y}}{ f_{c,f}}\bigg{)}+A_{sr}f_{sk} \tag{9}\]  
where \(t\) is the steel tube wall thickness, \(D\) is the outer diameter, and \(A_{sr}\) and \(f_{sk}\) are the area and the yield strength of the steel reinforcing bars, respectively. Coefficients \(\eta_{a}\) and \(\eta_{c}\) are expressed as \(\eta_{a}=0.25\big{(}3+2\overline{\lambda}\big{)}\leq 1.0\) and \(\eta_{c}=4.9-18.5\overline{\lambda}+17\overline{\lambda}^{2}\geq 0\). Relative slenderness can be calculated from \(\overline{\lambda}=(N_{pl,RK}/N_{cr})^{0.5}\), where the characteristic value of the plastic resistance to compression is \(N_{pl,RK}=A_{s}f_{y}+A_{c,c}f_{c,c}+A_{c,f}f_{c,f}+A_{sr}f_{sk}\), and the critical elastic normal force for the appropriate buckling mode is \(N_{cr}=\pi^{2}(E1)_{eff}/L^{2}\). The effective cross-section stiffness is defined as \((EI)_{eff}=E_{s}I_{s}+0.6(E_{c,c}I_{c,c}+E_{c,f}I_{c,f})+E_{sr}I_{sr}\), where \(I_{s}\), \(I_{c,c}\), \(I_{c,f}\), and \(I_{sr}\) are the second moments of area of the steel tube section, the core and the fill concrete section, and the steel reinforcement bars, respectively. Eurocode 4 approach is limited to members made from steel with strength classes between S235 to S460 and concrete of strength classes between C20/25 to C50/60.  
In this research, Eurocode 4 approach is extended over the concrete strength limits. Therefore, due to [42], the concrete strength reduction factor \(\eta\) is used. The effective compressive strength (\(f_{c,eff}\)) of high-strength concrete (\(f_{c}>50\) MPa) is determined from:  
\[f_{c,eff}=\eta f_{c} \tag{10}\]  
with the reduction factor \(\eta=1-(f_{c}-50)/200\). This method is limited to the concrete classes with a compressive strength of \(f_{c}<90\) MPa. In this way, the compressive cylinder strength of Fill 3 concrete was reduced from \(f_{c}=72.2\) MPa to \(f_{c,eff}=64.2\) MPa.
## 3 Results  
### Quality Loss Parameters  
#### 3.1.1 Chilling Injury  
The degree of CI index was larger with the progression of assessment times in all chemical and storage treatments (Table 1). However, fruit pre-treated with MeJA and SA had lower CI indices at \(p<0.05\) compared to water-treated fruit for both storage treatments. Fruit treated with MeJA showed significantly lower CI index in the CS treatments at day 21 and in the SL treatments at day 8 compared to corresponding SA treatments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{3}{c}{**CS at 1 \({}^{\circ}\)C**} & \multicolumn{3}{c}{**SL at 25 \({}^{\circ}\)C**} \\ \cline{2-6}  & **Day 7** & **Day 14** & **Day 21** & **Day 4** & **Day 8** \\ \hline CI index (\%) 1  & 16.68 a & 37.65 a & 18.67 a & 30.24 a \\
0.2 mmol MeJA & 1.35 a & 4.52 b & 9.68 c & 8.65 b & 14.35 c \\
2 mmol SA & 1.64 a & 5.58 b & 20.91 b & 7.68 b & 19.26 b \\ LSD\({}_{0.05}\) 2  & ns & 3.56 & 6.83 & 4.29 & 3.98 \\ FD index (\%) 3  & \\ Control & 16.05 a & 56.24 a & 96.25 a & 66.36 a & 100 a \\
0.2 mmol MeJA & 2.11 b & 6.57 b & 16.98 b & 16.35 b & 36.65 b \\
2 mmol SA & 2.33 b & 7.58 b & 15.61 b & 20.36 b & 35.36 b \\ LSD\({}_{0.05}\) & 1.74 & 5.45 & 4.72 & 8.31 & 3.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Indices of CI (chilling injury) and FD (fruit decay) values for the treatments of methyl jasmonate (MeJA, 0.2 mmol L\({}^{-1}\)) and salicylic acid (SA, 2 mmol L\({}^{-1}\)) on cultivar ‘Bergarouge’ apricot fruit at three cold storage (CS) dates (days 7, 14 and 21) and at two assessment dates (days 4 and 8) of shelf-life (SL) treatment.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### Segmentation-Based Observation Units
#### 3.2.1 Adaptive Filtering  
An adaptive edge-preserving filtering in further stand-level processing was tested. An own heuristic algorithm was implemented and used. It employs a set of alternative image windows (kernels) and selects the one with the smallest variance. The windows were selected inside a window of \(5\times 5\) pixels. The line and column coordinates for a set of 26 different windows are given in Table 5 in the groups of three pixels. For example, (\(-2\), \(-1\), \(0\)) (\(2\), \(1\), \(0\)) means the set of pixels (line, col) (\(-2\), \(2\)), (\(-2\), \(1\)), (\(-2\), \(0\)), (\(-1\), \(2\)), (\(-1\), \(1\)), (\(-1\), \(0\)), (\(0\), \(2\)), (\(0\), \(1\)) and (\(0\), \(0\)), the center pixel of the window, that is the pixel for which the average value is calculated and attached. Usually 3-5 consecutive runs, depending on data, are needed to identify homogeneous forest patches.  
The window with the smallest variance was selected and the average value was attached to the center, that is, the pixel in question. The windows allow the detection of narrow linear form structures in the target and preserves those structures.
# The Evolution of Volatile Memory Forensics
## 4 Memory Analysis
### Traditional Memory Forensic Approaches
#### 4.2.2 Dynamic Analysis within a Sandbox  
Sandboxing can provide a dynamic approach to cyber forensics. Malware is allowed to execute in a controlled environment, called a sandbox, and its behavior and characteristics, including information about its volatile memory, can be recorded [50]. Analyses of the information collected in the sandbox environments can be used to help identify future threats. Sandboxes are a necessary aspect of dynamic forensic techniques, as they protect the analysts' systems from unwanted infection. Bare-metal runs, malware runs outside a sandbox, are not practically scalable because they require a full system reinstall after each malware run to restore the analyst system to a clean slate.  
There are two main approaches used to set up a sandbox, virtualization and emulation, and they differ in how the controlled environment is created.  
Virtualized Environments  
Virtualized environments, called virtual machines, are controlled by a hypervisor. The hypervisor software controls the access of different programs to the underlying hardware, so the virtual machines can be isolated from other virtual machines or programs on the same hardware. However, the hypervisor and the virtual machine cannot be run at the same time, making it difficult to gather detailed data on the program's execution. The presence of the hypervisor is also difficult to hide from the malware, and malware authors are known to use evasive coding techniques that identify the presence of the hypervisor and subsequently modify the program's behavior [50].
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 3 Soundfield Recording and Reproduction
### 3D Soundfield Recording  
The soundfield microphone, arranged as a microphone unit composed of multiple microphone capsules and a signal processor, with 3D pick up capability is commonly used for soundfield recording. This microphone normally has a 3D geometry, such as the B-Format Ambisonic microphone [88] and EigenMike (a spherical microphone array) [89], as shown in Figure 3. The design of the microphone for soundfield recording is based on the decomposition of the 3D soundfield using Spherical Harmonics, i.e., Equation (1).
# Echocardiography in the Diagnosis and Management of Tricuspid Atresia
## 5 Echo-Doppler Studies Following Palliative/Corrective Interventions
### Echocardiographic Evaluation of Interstage Issues  
Interstage mortality has been reported to occur in 5 to 15% of patients [50; 51]. While the focus of these studies was on hypoplastic left heart syndrome, these principles are equally applicable to TA [36; 37; 38; 52]. Interstage mortality occurs more often between Stages I and II than between Stages II and III. The causes of interstage mortality are restrictive atrial communication, obstruction of the aortic arch, distortion/stenosis of the pulmonary arteries, atrio-ventricular valve insufficiency, shunt blockage, and intercurrent illnesses [50; 51; 52]. Echo-Doppler studies are useful in detecting these abnormalities and should be routinely used to identify these issues so that they can be effectively addressed with medical, transcatheter and/or surgical methods, as deemed appropriate for a given clinical scenario and, thus, avoid or reduce interstage mortality.  
Figure 30: **(A,B)** Selected video frames from suprasternal notch view demonstrating left sided bidirectional Glenn shunt. In **(A)**, the right (RPA) and left (LPA) pulmonary arteries are faintly seen by 2D. On color Doppler study, the visualization is slightly better (**B**). LSVC, left superior vena cava.  
Figure 29: Selected cine frames in postero–anterior (**a**) and sitting up (**b**) views, demonstrating bidirectional Glenn procedure (the superior vena cava [SVC] is anastomosed with the right pulmonary artery [RPA]) in two different patients during Stage II of Fontan procedure. Unobstructed flow from the SVC to the right (RPA) and left (LPA) pulmonary arteries is shown. Reproduced from [PERSON], _Indian J Pediatr_ 2015; 82:1147–1156 [36].
## 2 Related Works
### Playground Detection  
[PERSON] et al. suggested a basketball broadcasting system based on the players' trajectory tracking [8]. They made an application to broadcast basketball games based on tracking players by using a Video Panorama Image Shift and Kanade-Lucas-Tomasi (KLT) features to track the homography of the video. They showed an accuracy of 80.5% for overall IoU detection recognition. [PERSON], [PERSON] and [PERSON] et al. suggested a model dividing the court based on an end-to-end model and obtaining homography through camera pose estimation [9]. To track players, and to detect players and objects separately, models like YOLO have been constantly studied. They suggested a model to predict a homography by dividing the court and estimating the camera pose according to an end-to-end model. They showed an accuracy of 83.2%.  
[PERSON] et al. introduced a study for matching a football field [10]. This paper does not rely on the key features of a video. Instead, it suggests applying calibration by using DeepLabV3 and ResNet to obtain segmentation of points, lines, and circles and calculate the Segment Reprojection Loss iteratively to find the optimized camera parameters and lens distortion coefficient.  
In previous studies on recognizing sports arenas, algorithms were applied to accurately determine locations by generating panoramic images from camera footage collected at various angles and calculating the homography for each section. However, homography-based methods require sufficient information about the entire view during panoramic image generation. When such information is lacking, significant errors occur in recognizing straight court areas. Additionally, when spectators are present within the court area, it becomes challenging to accurately identify the boundaries. This necessitates either manually removing the spectators or applying straight lines to refine the boundary recognition, which poses additional challenges. In this study, we adopted a global view approach to recognize the entire arena and subsequently applied YOLO segmentation to enable detailed recognition of local views. This approach minimizes errors introduced by panoramic image generation.
## 3 Algorithm Validation
### Analysis of Results
#### 3.2.3 Low-Dimensional Multipeak Function  
The objective function identified in this paper is the low-dimensional, multi-peaked function (F7-F9). As shown in Table 2, the SSA algorithm is less stable, while the CSSOA algorithm has a better ability to jump out of the local solution. However, IESSA employs an irrational escape strategy and achieves better convergence accuracy, stability, and ability to jump out of the local optimal solution than CSSOA in the three selected test functions. Its efficiency is much more than that of SSA, GWO, and PSO.  
Apart from stability and convergence accuracy, the convergence speed is also an important factor in evaluating an algorithm's performance. The convergence speed of IESSA is shown in Figure 3. The number of iterations was set to \(T=300\), and the rest of the parameters were unchanged. The convergence speed comparison graphs of the three groups function as follows.  
In the test functions F1, F2, and F3, IESSA converged much faster than the remaining four algorithms and was able to converge to the optimal value of the function. For test function F4, IESSA exhibited a much higher convergence speed and ability to search for the global optimal solution compared to SSA, GWO, PSO, and CSSOA.  
Overall, the IESSA algorithm performed well in the function testing process. Compared with the SSA, GWO, PSO, and CSSOA algorithms, the IESSA algorithm can jump out of the local optimum and then search for the global optimum solution. The convergence speed and accuracy reveal that IESSA has an extremely strong ability to jump out of the local optimum solution; moreover, in terms of the mean standard deviation, the solution accuracy and stability of IESSA are stronger.