esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 9 Network Attacks in WSNs  
WSNs are subject to a variety of risks; attackers can interfere with radio transmissions by inserting their data bits into the link and replaying old packets, among other methods [40]. Attackers may put malicious nodes in the network with similar capabilities to normal nodes or catch typically deployed nodes and erase their memory [62]. Table 3 depicts a wide range of network security attacks mapped against network layers: the physical layer, data link layer, network layer, transport layer, and application layer. These network security attacks are categorized as routing-based and protocol-layer-based attacks. The type of network attack triggered by an attacker on any layer depends upon the layer type. These network layers consist of specific functions and restricted responsibilities; thus, an attack is carried out to stop the layer from working or to disrupt the layer's performance.  
Table 4 presents the features of specific attacks on WSN layers. The attack features show the damage executed upon each layer when attacked by a particular attacker. The attacks carried out on WSN layers are categorized into two types: internal and external. Most external attacks capture sensitive information by using malware such as worms, Trojan horse viruses, phishing, and other methods to obtain access to government and corporate websites, applications, and security systems. Network employees with connections to servers and classified documents who are dissatisfied are more likely to target and rob intellectual property. An insider threat occurs when a current or former employee, consultant, or business partner obtains access to an organization's network, system, or data and intentionally misuses them or when such an individual's access results in misuse.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Attacks** & **Layer** & **Routing-Based Attacks** \\ \hline
[70,71] & Interception, radio interference, jamming, tempering, Sybil assault & Physical layer & Sybil assault \\ \hline
[64,72] & Replay attack, jamming assault, spoofing, altering routing assault, Sybil assault, traffic analysis, monitoring, exhaustion, collision & Data link layer & Routing assault, Sybil assault \\ \hline
[73,74] & Black hole assault, worm hole assault, sink hole assault, gray hole assault, selective forwarding assault, hello flood assault, misdirection assault, internet smart assault, spoofing assault & Network layer & Black hole assault, worm hole assault, sink hole assault, gray hole assault, selective forwarding attack, hello flood assault \\ \hline
[75,76] & De-synchronization, transport layer flooding assault & Transport layer & — \\ \hline
[67,77] & Spoofing, path-based DoS, alter routing assault, false data ejection & Application layer & Alter routing assault \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of network attacks on WSN layers.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Print Quality Problems
#### 3.3.13 Blobs and Zits  
Blobs and zits are marks that appear on the outer shell surface of the part, at times within a specific location where the nozzle starts to deposit and return (Figure 13). Examine the deposition to check if the blobs appear at the starting point or at the end of each layer. Solutions include the following:  
1. Having proper retraction and coasting settings.
2. Selecting the best location for the deposition point.
3. Blobs that appear in the beginning could indicate that the extruder is priming too much plastic. Use a higher retraction setting with an extra length when restarting the print. This option determines the retraction distance when the extruder is stopping as well as the priming distance used when the extruder restarts. Reduce the priming distance by adding a negative value for the extra length during restart. For example, if the retraction distance is 1.0 mm, and the extra length of restart distance is \(-0.2\) mm, each time when the extruder stops, it retracts 1.0 mm ofmaterial. As the extruder starts to extrude, it will only push 0.8 mm of plastic into the nozzle. Adjust this setting until the defect no longer occurs when the extruder initially begins printing the perimeter [78].
4. If blobs appear at the end of a perimeter, turn on the wipe before retracting feature (Slic3r PE) or coasting (Cura) to enable the nozzle to move to the next starting point while the extruder retracts. Adjust the value between 0.2 and 0.5 mm [78].
5. Select a more suitable seam position. Set the start point of the perimeter loop, by choosing either Random, Nearest or Aligned positions [74,79].
## 5 Numerical Examples
The numerical results obtained by implementing the methods [3; 5] are also listed for comparison. Note that the elapsed CPU time for the same routine in Matlab may be different every time. In order to count the time accurately, we took the average of all runtimes of 100 independent runs as the elapsed CPU time. On one hand, our method is more accurate than [PERSON]'s method [3] and comparable to [PERSON]'s method [5]. On the other hand, the runtime of our method is less than that of [PERSON]'s method and comparable to [PERSON]'s method. This indicates that [PERSON]'s method can achieve the best precision in our experiment, while our method is superior in the term of elapsed CPU time. This is because we only need to solve a tridiagonal linear system instead of solving a penta-diagonal system in [5], which is much easier to implement. Clearly, the proposed method is efficient, therefore it provides a new approach to deal with second order linear differential equations. Figure 1: Numerical results in Example 2 (\(n=32\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{n}\)} & \multicolumn{2}{c}{**[PERSON] et al. [3]**} & \multicolumn{2}{c}{**[PERSON] et al. [5]**} & \multicolumn{2}{c}{**Our Method**} \\ \cline{2-7}  & \(\boldsymbol{e^{\infty}}\) & **Time(s)** & \(\boldsymbol{e^{\infty}}\) & **Time(s)** & \(\boldsymbol{e^{\infty}}\) & **Time(s)** \\ \hline
4 & \(4.946\times 10^{-2}\) & \(1.78\times 10^{-4}\) & \(5.927\times 10^{-3}\) & \(5.33\times 10^{-3}\) & \(2.710\times 10^{-3}\) & \(2.48\times 10^{-4}\) \\
8 & \(1.231\times 10^{-2}\) & \(1.89\times 10^{-4}\) & \(6.173\times 10^{-4}\) & \(5.34\times 10^{-3}\) & \(6.700\times 10^{-4}\) & \(2.62\times 10^{-4}\) \\
16 & \(3.081\times 10^{-3}\) & \(2.61\times 10^{-4}\) & \(1.797\times 10^{-4}\) & \(5.54\times 10^{-3}\) & \(1.670\times 10^{-4}\) & \(3.68\times 10^{-4}\) \\
32 & \(7.704\times 10^{-4}\) & \(3.43\times 10^{-4}\) & \(3.495\times 10^{-5}\) & \(5.72\times 10^{-3}\) & \(4.183\times 10^{-5}\) & \(5.94\times 10^{-4}\) \\
64 & \(1.926\times 10^{-4}\) & \(5.75\times 10^{-4}\) & \(6.702\times 10^{-6}\) & \(6.28\times 10^{-3}\) & \(1.046\times 10^{-5}\) & \(1.10\times 10^{-3}\) \\
128 & \(4.800\times 10^{-5}\) & \(1.62\times 10^{-3}\) & \(1.179\times 10^{-6}\) & \(7.36\times 10^{-3}\) & \(2.615\times 10^{-6}\) & \(2.25\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maximum errors and elapsed CPU times in Example 4.
# Implementation of Fault-Tolerant Control for
## 6 Experimental Results
### Experimental Results  
#### 6.3.1 Single Fault  
In this experiment, the fault occurred only at a single joint after 10 s. The desired fault function includes bias, and the fault is assumed to be  
\[\left\{\begin{array}{ll}\tau_{1}^{t}=\tau_{1}\\ \tau_{2}^{t}=(1-\rho_{2}(t))\tau_{2}+f_{2}(t)&\quad t>5000\\ \tau_{3}^{t}=\tau_{3}\end{array}\right. \tag{55}\]  
where \(\rho_{2}(t)=0.4\sin(\pi t/2400)\) and \(f_{2}(t)=-60\sin(\pi t/2400)\).  
Figure 9: The experimental setup with FARA-AT2 Robot.  
The results are shown in Figures 10-12. First, in Figure 10, the fault estimation is presented where the dashed aqua and pink lines are the upper and lower thresholds, respectively. The estimation results of the uncertainties and disturbance were within the thresholds for Joints 1 and 3, and for Joint 2 before the fault occurred. In contrast to the simulation results, the fault estimation results were not close to 0 before the fault occurred because in practice, relatively high value for the uncertainties and disturbances always exist. Therefore, using such thresholds is a good method for monitoring when faults occur in real applications. As shown in Figure 10, the extended state observer estimated the fault well. In Figure 11, the tracking error performance at each joint is shown. Before the fault occurred, the tracking trajectory of the proposed AFTC had no advantage over the AFTC with conventional sliding mode control. However, after the fault occurred, the advantages of the proposed AFTC were observed; the error at Joint 2 with AFTC-SSMC was smaller than that of AFTC-SMC, and the joint error was approximately 0.005 rad when the fault occurred. This means that the proposed controller has the ability to prevent tracking errors from getting larger due to a fault, and thus averting a critical condition (Figure 11b); the Joint 2 error of the AFTC-SSMC was reduced when compared to that of the AFTC-SMC after faults. However, the Joints 1 and 3 errors of AFTC-SSMC were slightly increased compared with those of AFTC-SMC after faults (Figure 11a,c). This is probably due to the synchronization techniques, which tend to drive all joint errors to be comparable. The proposed synchronous control seems well-suited for fault tolerant control. In addition, the synchronization joint errors theoretically approached zero, and as shown in Figure 12, they remained less than \(\sim\)0.0025 rad in this experiment.
# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 5 Results and Discussion
### Comparison of Performance Based on F1 Score with and Without Sampling  
In our evaluation, the F1 score served as a critical measure of our hybrid stacking ML+DL model's performance with and without various sampling techniques. The results indicate a notable variation in the model's performance depending on the sampling method applied.  
The comparison between the hybrid ML+DL model with and without sampling highlights the significant impact of sampling techniques on performance metrics. Without sampling, the hybrid model achieves an F1 score of 90.11%, with high accuracy (99.97%) and precision (97.62%) but a lower recall (83.67%) compared to models with sampling. In contrast, applying sampling techniques like Borderline-SMOTE and SMOTEENN enhances the F1 score to 94.63% and 93.90%, respectively, by improving recall to 89.85% and 88.56%, while maintaining exceptional precision (over 99.90%). Other sampling methods, such as ROS, also improve the F1 score (92.89%) and recall (86.75%), though techniques like Tomek Links and ENN prioritise precision, achieving 100% and 98.70%, respectively, but at the cost of lower recall (71.43% and 77.55%). Overall, sampling techniques significantly enhance the model's ability to detect fraudulent transactions (recall), with Borderline-SMOTE and SMOTEENN offering the best balance between precision and recall, resulting in superior F1 scores. While the novel hybrid stacking ML+DL model performs well on its own, the inclusion of appropriate sampling methods, such as Bordersmote and SMOTEENN, can further optimise its performance, making it more adept at identifying fraudulent activities while minimising false positives.  
In summary, the strength of the novel hybrid stacking ML+DL model lies in its sophisticated architecture that leverages both traditional ML techniques and advanced DL approaches. Incorporating a variety of ML techniques such as DT, RF, SVM, XGBoost, CatBoost, and LR, the model effectively captures patterns and makes accurate predictions. Additionally, the integration of CNNs and BiLSTM networks, equipped with attention mechanisms, enables the model manage transaction sequences. This comprehensive approach ensures dynamic feature extraction, stability, efficiency, and interpretability, making the novel hybrid stacking ML+DL model a potent instrument for addressing intricate binary classification challenges in the detection of credit card cyber fraud.
## 2 Related Studies
### Pest Detection and Classification Frameworks
In [27], a module of the YOLOv5 neck model was changed to process features in a more flexible manner, and an attention mechanism module is introduced for capturing more feature information from the backbone. The classical YOLOv7 approachhas been modified in [28] by introducing the Cross-Stage Partial Network as a backbone to enhance feature learning by splitting feature maps and merging them through cross-stage connections. The YOLO algorithm is also used in [29] for the development of a real-time insect classification procedure to be installed on mobile phones. The system was conceived to work both in on-line and off-line modalities so as to monitor farmlands located in limited to no internet/4G/5G connection zones. For fine-grained image classification processes, an attention-selection mechanism is implemented in [30]. In more detail, [PERSON] et al. [30] developed a tool which uses a convolutional neural network (CNN) and visual and swin-transformers as backbones for regions of interest localization in the images under test by Gradient-Weighted Class Activation Mapping. Feature extraction from important regions is based on the attention information process and makes the reduction of background noise interference possible. In this way, only local region information of the tested image are necessary, and these are utilized as input to the Support Vector Machine classifier for insect recognition. Furthermore, in [31], a transformer is used to capture global contextual information, thus avoiding the limits of classical convolutional architectures. Unlike the previous study, a non-linear Support Vector Machine classifier with a Power Mean Kernel in combination with fine-tuned EfficientNets are implemented in [32] for the development of a pest classification system. In this way, the training task revolves around the reduction of margin-based loss instead of the decrease in cross-entropy loss. To improve the method's robustness in the presence of occluded and distorted images, the authors in [33] conceived a system composed of a global feature extraction network, visual regeneration network, feature fusion module, and a classifier. Patch-based augmentation, pre-trained feature extraction, and distortion information are utilized for achieving the objective. After the creation of an augmented dataset, the visual regeneration network is used for pre-training and capturing low-level semantic features. The fusion of multilevel features coming from the feature extraction and regeneration network makes the model able to accurately recognize insects in real images. Saliency maps are used in [34; 35] for improving the method's performance. In particular, [PERSON] et al. [34] have resorted to saliency maps to lead a weakly supervised multiple instance learning method for automatically pinpointing regions of interest in images under test, while [PERSON] et al. [35] adopted fine-grained saliency during the preprocessing phase.
## 3 Preliminaries
### Chaotic Maps
#### 3.3.2 Enhanced Chebyshev Maps  
The cryptographic system, which is based on chaotic maps, has proven to be more efficient than traditional cryptographic systems that use modular exponential operations and point multiplication operations on elliptic curves [26; 27; 28; 29; 30; 31]. Enhanced Chebyshev polynomials have semi-group and commutative properties and suffer from the discrete logarithm problem, the Diffie-Hellmann problem, and the inverse assumption.  
After the security problems of the Chebyshev polynomial were raised by [PERSON] et al. [27], [PERSON] [31] proposed an improved Chebyshev polynomial and proved that if that Chebyshev polynomial is defined not in the interval \([-1,+1]\) but in \((-\infty,+\infty)\), then the security issue can be solved, and the characteristics are maintained. The definition and characteristics of the Chebyshev polynomials and their enhancement are described as follows.  
\[\left\{\begin{array}{c}T_{0}(x)=1;\\ T_{1}(x)=x;\text{and}\\ T_{n}(x)=2 xT_{n-1}(x)-T_{n-2}(x)\bmod p,\text{ for }n\geq 2,\end{array}\right.\]  
when \(x\in(-\infty,+\infty)\) and \(p\) is a large prime number. Chebyshev polynomial satisfies the Semi-Group characteristics (closure and associativity) and the commutative rate.  
\[T_{r}(T_{s}(x))\equiv T_{r\cdot s}(x)\equiv T_{s}(T_{r}(x))\bmod p.\]  
(1) Extended Chaotic Map-Based Discrete Logarithm Problem  
The variable \(y\) is the result of the operation of Chebyshev polynomial degree as \(r\). When \(x\), \(y\), and \(p\) are known, it's difficult to find an integer \(r\) to satisfy the following formula:  
\[T_{r}(x)\equiv y\ (\bmod p)\]  
(2) Extended Chaotic Map-Based Diffie-Hellman Problem:  
Knowing that \(T_{u}(x),T_{v}(x),T(.)\), \(x\) and \(p\), when \(u\), \(v\geq 2\) and \(x\in(-\infty,+\infty)\), \(p\) is a large prime number, it is difficult to find the following formula:  
\[T_{u\cdot v}(x)\equiv T_{u}(T_{v}(x))\equiv T_{v}(T_{u}(x))\bmod p.\]  
(3) Extended Chaotic Map-Based Inverse Assumption:  
Since the Chebyshev polynomial satisfies the semi-group (closure and associativity), it is known that \(u\), \(v\), \(T(.)\), \(x\) and \(p\); \(u\), \(v\geq 2\); \(x\in(-\infty,+\infty)\), and \(p\) is a large prime number, it is difficult to find an integer \(u^{-1}\) that satisfies the following formula:  
\[T_{u^{-1}}(T_{u\cdot v}(x))\equiv T_{u^{-1}\cdot u\cdot v}(x)\equiv T_{v}(x) \bmod p.\]
## 3 Results and Discussion
### Production of eLpp-OmpA-AppA and Display of AppA
#### 3.1.2 Expression of eLpp-OmpA-AppA and Display of AppA  
Recombinant protein production using a pET expression system requires _E. coli_ cells containing the \(\lambda\)DE3 lysogen, which carries the T7 RNAP-encoding gene, with BL21(DE3) being the most widely used strain [56; 55]. In this host, the IPTG-inducible lacUV5 promoter controls T7 RNAP production, whereas expression of the recombinant protein (encoded by a gene located in a pET-based plasmid) is regulated by a T7 RNAP-specific promoter [56]. Despite the advantages of BL21(DE3), it is unsuitable for producing recombinant proteins linked to growth problems (e.g., toxic or membrane proteins). Lemo21(DE3), an improved strain derived from BL21(DE3), has been developed to address these issues. This host harbors a plasmid encoding T7 lysozyme (T7 Lys) under control of the rhamnose promoter [56; 57; 58]. In this host, the transcriptional activity of T7 RNAP is controlled by the cellular abundance of T7 Lys, a physiological inhibitor [59; 60].  
Results from our previous study showed that T7 RNAP overload negatively affects the viability of BL21(DE3) cells harboring a pESE-derived plasmid (thus expressing the eLpp-OmpA carrier linked to the outer membrane), and basal co-expression with T7 Lys, eased this situation, as evidenced by the ordinary growth kinetics of Lemo21(DE3) cells subjected to similar experimental conditions [41]. Therefore, this improved strain was used as a host for pESE-AppA (i.e., the biofactory for eLpp-OmpA-AppA production) and as a cell source to assay biomembrane-linked enzyme activities.  
_E. coli_ Lemo21(DE3) cells harboring the pESE-AppA construct or an internal control plasmid, either pET22b(+) or pESE, were used to determine protein production using standard protocols. Sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE)  
Figure 2: Schematic representation of eLpp-OmpA-AppA. Lpp (yellow), signal peptide plus nine residues of Lpp; OmpA (green), transmembrane region (\(\beta\)1-\(\beta\)7) of OmpA; Myc (cyan), cMyc epitope tag; AppA (blue), mature polypeptide of AppA.  
analysis revealed the consistent expression of eLpp-OmpA and eLpp-OmpA-AppA, which appeared as strongly stained bands in the respective cell extracts (Figure 3a). Fluorescence microscopic visualization of non-permeabilized bacteria stained with a fluorescein isothiocyanate (FITC)-conjugated anti-Myc(tag) antibody confirmed the surface display, as the cells expressing eLpp-OmpA or eLpp-OmpA-AppA, which extended the Myc epitope towards the extracellular space, emitted intense signals (Figure 3b). Furthermore, as a quantitative trait, bacteria expressing eLpp-OmpA-AppA protein showed a significant WC-linked acid phosphatase activity (_p_ < 0.01), validating the functional display of AppA on the cell surface (Figure 3c).
## 2 Rotational Flow Control
### Operational Robustness  
The critical frequencies \(\Omega\) (5), (6) of rotationally controlled valves depend on a number of parameters \(\{\gamma_{k}\}\), such as the loaded liquid volume \(U_{0}\) (its density \(\varrho\) is fairly constant for aqueous solutions at room temperature), the structural dimensions of \(\Gamma\), and the quantities determining the contributing pressure contributions (1), (2) and \(p_{0}\). Experimentally, each input parameter \(\gamma_{k}\) thus displays a normal distribution of standard deviation \(\Delta\gamma_{k}\). Consequently, also the resulting critical spin rate \(\Omega\) (5) also displays the following spread:  
\[\Delta\Omega\approx\sqrt{\sum_{k}\left(\frac{\partial\Omega}{\partial\gamma_{ k}}.\Delta\gamma_{k}\right)^{2}} \tag{7}\]  
as approximated by Gaussian error propagation, assuming small \(\{\Delta\gamma_{k}\}\) and mutually independent \(\{\gamma_{k}\}\). Operationally robust valving can thus only be assured if the spin rate \(\omega\) is moved across the frequency band \(\Omega\pm M\cdot\Delta\Omega\); the factor \(M\) determines the level of functional robustness \(P_{M}\) evaluated by \(1-\text{erf}\!\left[M/\sqrt{2}\right]\) with the error function \"erf\"; so, for \(M\in\{1,2,3,4,\ldots\}\), valving reliability can be gauged at \(P_{M}\approx\{68\%,95\%,99.7\%,99.99\%,\ldots\}\).  
Figure 1: Centrifugo-pneumatic (CP) dissolvable-film (DF) siphon valve structure structure \(\Gamma\) (linearized display, dimensions not to scale with typical measures listed in the Appendix A.1). The depths \(d_{0}=d_{\text{iso}}=d\) of all components upstream of the crest point at \(r=R_{\text{crest}}\) amount to 1 mm, and 200 μm thereafter. (**a**) In a first step, a liquid volume \(U_{\text{iso}}\) is loaded to the basic structure \(\Gamma\) which pneumatically isolates the inlet reservoir that is open to atmosphere at \(p_{0}\) from the downstream compression chamber. For instance, due to dynamic effects, the actual pressure \(p_{0}^{\prime}\) in the enclosed volume can (slightly) deviate from \(p_{0}\). (**b**) The liquid volume \(U_{0}\gg U_{\text{iso}}\) is retained upstream of the crest point at \(R_{\text{crest}}=R-Z\) for spin rates \(\omega<\Omega\). (**c**) The high-pass CP-DF siphon valve opens for \(\omega>\Omega^{*}\approx\Omega\) upon arrival of a minimum volume \(U_{\text{DF}}\) in the shallow DF chamber (\(d_{\text{DF}}\ll d\)). (**d**) Multi-segmented version for illustrating the key geometrical features for enhanced design optimization according to a given set of metrics.