esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 8 Case Studies  
### Adopting Content for Teaching and Learning in Higher Education  
#### 8.1.1 Teesside University  
Teesside University is a higher education institution in the North-East of England. According to the British education system, Teesside University provides many programs in computer science and sub-domains ranging from Level 4 to Level 8.  
The undergraduate course entitled \"Cybersecurity and Networks\" aims at training professionals in protecting systems and networks from digital attacks. The degree provides a hands-on approach and places a strong emphasis on cybersecurity, including ethical hacking and network infrastructures, particularly, ways to create and manage secure systems and networks. Students explore a range of different cybersecurity and networking techniques applied to different networks, hardware, and software platforms during their course.  
Examples of these techniques include:  
* How to effectively perform an effective, ethical hacking and security analysis;
* How to design and implement different network architectures used in industry, e.g., virtualized, mobile, and cloud-based systems;
* How to deliver excellent server administration and enterprise server management;
* How to secure computers and networks and implement intrusion detection/ prevention systems;
* How to perform risk assessment, data governance, and compliance.  
In the context of networks security, students explore different approaches and solutions.  
In some modules, they experiment with fundamental theories and basic applications. In other words, they apply their knowledge to a near-real-world scenario. In particular, in a module called \"CIS CYBER\", students work in small teams to plan, design, and build a secure network. Each team is required to design, implement, and document a secure network, including the installation of virtualized servers running a variety of operating systems. The module requires a professional approach to plan a successful development project, informed by current industry practice.  
In this context, as part of the network design, the student must identify and deploy solutions for the implementation of infrastructural services, such as routing, firewalling, intrusion detection/prevention, and others. Therefore, they use tools such as pfSense to implement routing services, network address translation, and Snort for intrusion detection and prevention. As part of their project, teams must conduct a security assessment of their system and perform a pen testing activity on another team's system. Thus, they can compare their findings with the one from the team who pen-tested them. This approach helps the student to develop critical thinking and understand real-world scenarios of system attacking and defending in the cyber landscape.
## 3 Data Sources and Research Methods
### Research Methodology
#### 3.2.4 Network Growth Optimization Model  
The research on network growth optimization is based on the following indicators, including average path length, degree value, betweenness centrality, clustering coefficient, closeness centrality, etc. The definition of network extension capability can usually be expressed as:  
\[A^{i}=m_{D_{\min}^{stable}}^{i} \tag{4}\]  
\[D=\max\{S_{ij}\} \tag{5}\]  
\(A^{i}\) represents the network expansion capability. As the desired number of nodes \(m\) increases, the network keeps a particular feature constant and minimizes the value. \(D\) stands for network diameter, which is the largest of all calculated path lengths and is a widely used indicator.  
These commonly used six kinds of optimization strategies are connect the node pair have the maximum shortest path length (strategy 1), connect the node pair with the minimum multiplied degree value (strategy 2), connect node pairs with smallest betweenness value (strategy 3), connect the node pair with minimum degree value (strategy 4), connect the node pair with smallest clustering coefficient (strategy 5), and connect the node pair with minimum closeness centrality (strategy 6).
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 6 The Measurements and Operating Efficiency Analysis
### Efficiency Plots for Forward Mode and Reverse Mode  
Figure 20 shows the efficiency curves with battery voltages \(V_{Bat}\) being 280, 340, and 403 V and charging currents ranging from 0.5 to 2.5 A. This reveals that the maximum conversion efficiency is 91.96% at the lowest battery voltage 280 V, and the maximum conversion efficiency is 93.25% at the highest battery voltage 403 V. The maximal efficiency of 93.67% appears in the case of the 340 V battery voltage because it gets near to the resonant point, and the total impedance is nearly 0. In the forward charging mode, the voltage of DC grid is 400 V, and the designed resonant frequency is around 100 kHz at the 340 V battery voltage, thus getting higher efficiency due to the switching instants of the turn-on and cut-off being near the zero-crossing point. When the discharging current is low, although possessing both ZVS and ZCS, the effect is not obvious due to low current. As the current rises to 1.5A, the efficiency at battery voltage 403 V apparently surpasses that with 280 V battery voltage.  
Figure 19: Waveforms of \(v_{DB4}\) and \(i_{DB4}\) with two discharging battery voltages: (a) \(V_{Bat}\) = 280 V; (b) \(V_{Bat}\) = 403 V.  
Figure 21 shows the efficiency curves with discharging voltages \(V_{Bat}\) being 280, 340, and 403 V and discharging currents ranging from 0.5 to 2.5 A. In the reverse discharging mode, the voltage of DC grid is 400 V as well. For low battery voltage level, it needs more voltage gain to fulfill DC grid charging and operates in the Region II due to low operating frequency. At different voltage levels of the battery, low battery voltage conditions need more current to discharge the constant voltage DC grid to achieve the identical power level and thus experience more relative power loss. In the case of a 280 V battery voltage, despite the converter operating in Region II and possessing ZCS on the rectifier side, its power loss rate is relatively higher. Operating at a 340 V battery voltage, even though the converter works near the resonant frequency and possesses the advantage of ZVS and ZCS, the conversion efficiency is better than that of the 403 V battery voltage due to more discharging current being needed for the identical power conversion. In fact, when the battery voltage is 403 V and operates at high frequency in Region I with only a ZVS mechanism, it achieves similar efficiency with that of 340 V. This reveals that the maximum conversion efficiency is 91.3% at the lowest discharging voltage 280 V, and the maximum conversion efficiency is 94.6% at the highest discharging voltage 403 V.
## Unknown experiments
### Current Limitations and Future Work  
While the presented approach demonstrates strong performance and accurate predictions within the tested setup, several limitations remain that offer opportunities for further enhancement of the model's robustness and generalizability.  
* **Material specificity:** As described in Section 2.1, all experiments were carried out using a single suspension, perlite, to ensure consistent results. However, other suspensions such as Rieselgur exhibit significantly different behaviours due to their high porosity and fluid retention characteristics. This may affect the direct applicability of the current model to a broader range of materials.
* **Limited scalability:** The experiments were performed using a chamber filter press with a plate size of 300 mm. While the current model has shown that it can adapt to different configurations if such setups are included during training, its ability to  
Figure 11: Comparison of measured (M) and predicted (P) flow rate for the experiments listed in Table 3. (**a**) illustrates the reduction in flow rate with a higher number of filter cycles. (**b**) shows the reduction in the flow rate with different concentrations and similar filter cycles over the pressure. (**c**) shows the increase in flow rate with different numbers of filter chambers. (**d**) presents the prediction of the flow rate for an unknown concentration of 15 g/L.  
generalize to presses of different sizes remains dependent on the diversity of the training data. Without sufficient variation, scaling the model to untested press sizes or configurations may be less reliable.
* **Hardware dependency:** The predictive accuracy of the model is influenced by the mechanical state of the system. Irregularities, such as inefficiencies in the membrane pump, leaks, or hardware degradation, can affect performance and introduce noise into the training data. If such anomalies remain undetected, they could gradually influence model performance.  
To address these limitations, future work will focus on expanding the dataset to include a wider variety of suspensions, operating conditions, and press configurations. Thanks to the installed sensor setup from Section 2.5, the experimental data is continuously collected and stored in a central database, allowing automatic model updates as new operational scenarios arise. Incorporating anomaly detection and additional sensor types can further improve resilience to hardware-related issues. Ultimately, a more diverse and representative dataset is expected to not only improve model accuracy but also enhance its ability to generalize to unseen scenarios. In addition, we will explore whether it is possible to normalize relevant features and upscale the existing model to different filter press sizes without requiring retraining from scratch. Furthermore, the use of physics-informed neural networks incorporating domain equations such as the Darcy law will be explored, particularly for modelling porous materials such as kieselgur, where fluid-solid interactions play a more dominant role.
###### Abstract  
As the requirement for image uploads in various systems continues to grow, image segmentation has become a critical task for subsequent operations. Balancing the efficiency and accuracy of image segmentation is a persistent challenge. This paper focuses on threshold-based grayscale image segmentation methods and proposes a fully automated approach. The approach begins with the implementation of an improved OTSU algorithm to determine the optimal dynamic threshold, enabling the segmentation process to adjust adaptively to varying image backgrounds. A novel method for selecting image center points is introduced to address the issue of poor segmentation when the center point falls outside the segmentation foreground area. To further enhance the algorithm's generalization capability and accuracy, a continuity detection-based method is developed to determine the start and end points of the segmentation foreground. Compared with traditional algorithms, tests on sample images of four different scales revealed that the proposed algorithm achieved average improvements in accuracy, precision, and recall rates of 14.97%, 1.28%, and 17.33%, respectively, with processing speed remaining largely unaffected. Ablation experiments further validated the effectiveness of using different strategy combinations, with the combination of all three strategies resulting in significant improvements in accuracy and recall rates by 15.51% and 16.72%, respectively.  
dynamic thresholds; shadow detection mechanism; OTSU algorithm; center point selection method; continuity detection; threshold-based image segmentation
## 3 Results  
### Base Strength and Basicity  
The results of the base strength and basicity of AC, Ca/AC, Na/AC, and CaNa/AC are shown in Table 1, which suggested that AC was not basic. However, Ca/AC was alkalescent with the basic strength of H_ = 7.2-9.8, indicating that Ca(OH)2 neutralized most of the acid sites in AC and replaced them with conjugated basic sites during the modification process. Ca/AC exhibited inferior catalytic activity in the transesterification reaction due to its weak H_[30; 31]. Compared with Ca/AC, CaNa/AC presented a higher basic strength and basicity, which indicated that the amount of stronger basic sites increased with the introduction of Na, thereby resulting in an improvement in catalytic activity. Moreover, the oil conversion of Na/AC (58.6%) was larger than that of Ca/AC, which is due to the fact that the introduction of NaOH increased the basicity, which contributed to the higher oil conversion. The synergistic effect of both sodium and calcium attributed to the highest base strength of CaNa/AC.
## 3 Higher-Layer Protocols: MAC & Routing  
After reviewing and analyzing several acoustic propagation models, in this section we will introduce several higher network layer protocols that we will use later in our experiments. Most of the higher protocols presented below were proposed for terrestrial network technologies, and their network performance is well-known. However, as stated in the introduction, UWSNs exhibit several architectural differences with respect to the terrestrial ones. In particular, the acoustic signal propagation delay is several orders of magnitude higher than RF signals, resulting in large signal propagation _versus_ packet transmission time ratios, something which it is not desirable for obtaining high utilization levels of network resources, and as a consequence limiting the overall network performance. So, the behavior of higher level protocols may seriously change when they are employed in UWSN scenarios.  
Figure 3: Attenuation of acoustic waves.  
As with acoustic propagation models, the selected protocols for the evaluation include: (a) several MAC layer protocols: from simple proposals, like ALOHA that only cares of sending the packet message, until more complex protocols like DACAP that include carrier sense and collision avoidance by means of at least three-way handshake exchanges; and (b) a couple of routing protocols: a simple static routing and a cross-layer routing proposal FBR. We will test all of them to determine how the scenario environmental changes impact on their performance in terms of throughput and delay.
## 3 Results
### Line-of-Sight Analysis for Underground Mine
The acquired data were collected into the coordinator module. Further, an additional coordinator module was installed to distribute the data and prevent missing data owing to congestion. Figure (b)b shows an example of the vibration results obtained using the monitoring module. As the module was installed after performing LOS analysis in advance, stable communication was achieved, and no data were missing from most monitoring devices. Figure 8: Results of scanning the surroundings at an underground mine site using portable LiDAR (VLP-16). Numbers 1 to 6 are the results for some points where monitoring devices were installed. Figure 7: Results of communicable area analysis at certain points where the IoT monitoring devices are installed. Numbers 1 to 6 are the results for some points where monitoring devices were installed, and each red box indicates the corresponding area in Figures 8 and 9.
## 3 Results and Discussion
### X-Ray Diffraction Analysis  
Figure 9a shows the XRD test results of all samples on Day 1. The figure indicates that the main products of the cement's hydration reaction are AFt, Portlandite, and Monocarboluminate. Additionally, calcium carbonate was detected from the limestone in the cement used.  
Figure 8: (**a**) Time to reach the main peak; (**b**) peak intensity.  
Figure 7: (**a**) Hydration heat flow development normalized by cement mass fraction (**b**) according to the quality fraction of cement cumulative heat of hydration.  
Figure 9a shows that the quartz peak is pronounced with the addition of the QZ component. By comparing the XRD spectrum of the raw material QZ, it can be determined that these quartz peaks originate from QZ. In addition, the figures show the peaks of C\({}_{3}\)S and C\({}_{2}\)S. This is because the hydration reaction is still incomplete, and the mixture still contains unreacted C\({}_{3}\)S and C\({}_{2}\)S. A comparison of XRD patterns of the various samples  
Figure 9: XRD patterns of the blended pastes at (**a**) 1 day and (**b**) 28 days.  
in the figure shows that except for the quartz peak of the added QZ component, which is apparent, the types of other reaction products are similar. This indicates that the addition of FA and QZ only changes the content of hydration products but does not change the type of hydration products.  
Figure 9b shows the XRD test results of all samples after 28 days. Comparing Figure 9a,b shows that the peak value of AFt decreases from 1 day to 28 days because AFt is converted into a more stable AFm. In addition, the peak values of C\({}_{3}\)S and C\({}_{2}\)S decreased because after 28 days of hydration, the C\({}_{3}\)S and C\({}_{2}\)S in the reactants were consumed, and other hydration products were generated. The figure also shows that the intensity of the quartz peak has no noticeable change, which indicates that QZ is an inert material and has difficulty chemically reacting with cement components. Finally, as the curing time increased from 1 day to 28 days, the diffraction peak of monoaluminate was observed, while the peak intensity of calcium carbonate weakened, indicating that calcium carbonate was consumed. This is because the calcium carbonate in the mixture reacted with aluminate to form monoaluminate [49]; the reaction equation is shown in Equation (4):  
\[\text{CH}+\text{CC}+\text{H}+\text{A}\rightarrow\text{C}_{3}\text{A-CC}\cdot \text{H}_{12} \tag{4}\]  
where CH is portlandite, CC is calcium carbonate, H is water, and A is the aluminum phase in cement.
# Learning Optimal Time Series Combination and Pre-Processing by Smart Joins
## 5 Experimental Results
### Results on Synthetic Data  
For synthetic data, different time series joining methods were used separately and the error, defined by Equation (2), was calculated for each method using windows of \(t\in(p,p+2]\) with \(p\in\{0,2,\ldots,38\}\). The selected values for the parameters of smart join methodology were \(w_{i}=1/7\ \forall i\) (i.e., the same importance for all different functions taking part in the error calculation) and the imputation function was \(f=\textit{ffill}\).  
Table 7 shows the error values per method and time window. The optimal solution (minimum error) is marked in bold. An additional column labelled \"theoretical\" represents the theoretically optimal solution. Thus, the obtained optimal solution in each time window can be compared and contrasted with the theoretical solution. In Figure 10 numerical results are shown graphically.  
As per Table 7, the optimal joining method (the one that has minimal error in each window) depends on the controlled distortion introduced. The proposed methodology is capable of obtaining as one of the optimal available results the theoretical solution. On the one hand, for \(t\in(0,10]\), the data was already available for the needed temporal reference system and for that reason all the methods were able to obtain a 0.0 value error. On the other hand, for \(t\in(10,20]\), as data points are removed randomly, there was no optimal theoretical solution, as from known data points the joining method should not be able to reconstruct the time series. For this range, the optimal solution for the joining method depends on \(\textit{Diff}(y,\vec{y})\), i.e., the distortion introduced is comparable to the one obtained with the lineal interpolation result. For \(t\in(20,30]\) and \(t\in(30,40]\) the optimal theoretical solutions were backward and forward join, respectively. However, in multiple windows, the nearest join method obtained the same solution as the theoretically optimal method, as the shifted data points  
Figure 9: Original data of the feature with id 14 from Figure 8.  
(\(t_{OI}\) introduced in the smart join system) are the nearest ones to the desired data points (\(t_{Dk}\) output temporal reference system).
## 2 Main Results
### Hybrid PID Controller with Smooth Transition to the Setpoint
#### 2.3.2 The Exponential Trajectory to the Setpoint
: _The proposed hybrid PID controller performs the same core operations as the conventional PID controller but introduces an additional step: the evaluation of an exponential reference trajectory at each time instant. This step involves a single exponential function computation and a simple reset condition, typically implemented using a modulo operation and a few multiplications. The added computational cost is minimal and easily manageable in modern control hardware._
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
## 3 Theoretical Methods
### Coulomb-Corrected Three-Step Model  
HHG is well described by the Lewenstein model [PERSON] (1999) and by the three-step model in the classical limit [PERSON] (1999). For a description of XUV-initiated HHG, the role of the XUV pulse in the ionization step, the influence of the Coulomb potential, and the role of the excited states must be accounted for. For that, a Coulomb-corrected three-step model for XUV-initiated HHG can be used [PERSON] _et al._ (2016). Here the discrete harmonics of the incident APT lead to the excitation of a ground-state electron to the continuum. Motivated by theoretical predictions of strongly broadened excited states in the presence of a strong IR field [PERSON] _et al._ (2016); [PERSON] _et al._ (2016), we describe the singly-excited states of helium as a continuum and allow XUV-driven transitions to those states. We note that in accordance with the results of Reference [PERSON] _et al._ (2016), we do not consider a difference between excitations to this effective continuum--driven by incident harmonics H11, H13, and H15--and photoionization to the continuum above the ionization threshold--driven by H17. After excitation, the electron propagates under the combined influence of the IR field and the Coulomb potential and undergoes over-the-barrier emission [PERSON] _et al._ (2016). The photoelectron recollides with the ionic core, sending off its energy as an XUV photon. In the CCTSM, several quantum paths contribute to the emission of a final harmonic, in three distinct ways. First, each incident harmonic opens up an ionization channel, leading to the appearance of an electron in the continuum with a well-defined energy. Second, the liberated electron either receives a kick in the direction of the Lorentz force exerted by the IR field (\"downhill\" trajectory branch) or against it (\"uphill\" trajectory branch) [PERSON] _et al._ (2016). Third, in analogy to tunneling-driven HHG, recollision is mediated by short and long trajectories. Quantum interference due to these multiple pathways leads to oscillations of the harmonic yield with changing XUV-IR delay. CCTSM is able to predict the delay-dependent sub-cycle oscillations and underlying phases of the contributing trajectories (for more information see Appendix A). An intrinsic interferometer in XUV-initiated HHG was used for reconstructing the photoionization dynamics in the presence of a strong IR field, finding good agreement between experiments and short-trajectory CCTSM calculations [PERSON] _et al._ (2016).