esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Blackout Analysis
### Initiating Events
#### 4.2.4 Human Factor (INIT_4)  
Human error is another possible cause which can lead to the initiation of a blackout. Even though these incidents are uncommon, they have a major impact on the safety of the power system. The cause of the blackout in Europe in 2006 was a dispatcher's mistake [13]. The momentary disconnection of the European interconnected power grid was caused by the switching off of a power line over the Ems River in western Germany to allow a cruise ship to pass through. Unfortunately, the dispatcher in the regional control center had not taken into consideration the N-1 operating reliability criterion, which overloaded the neighboring power line and caused the trip of its circuit breakers. The result was Europe to be divided into three separate regions, as depicted in Figure 4.  
Human error, as described before, is generally unintentional. However, there have been human intentions that caused blackouts with terrorist actions, such as the cyber-attack from Russia that caused the Ukrainian blackout in December 2015.  
In Table 4, the preconditions and the initiating events for all the major blackouts that occurred in Europe during the past 20 years are presented in detail.  
Figure 4: Schematic map of ENTSO-E (former UCTE) area split into three areas on 4 November 2006.
## 4 Numerical Optimization Experiments
### Benchmark Functions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**F** & **Test Function** & \(Dim\) & \(S\) & \(f_{\min}\) & **Group** \\ \hline F1 & \(f_{1}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}x_{i}^{2}\) & 30 & \([-100,100]^{n}\) & 0 & Unimodal \\ F2 & \(f_{2}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}[x_{i}]+\sum\limits_{i=1}^{ n}[1x_{i}]\) & 30 & \([-10,10]^{n}\) & 0 & Unimodal \\ F3 & \(f_{3}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}\left(\sum\limits_{i=1}^{ n}[1x_{i}]^{2}\right)\) & 30 & \([-100,100]^{n}\) & 0 & Unimodal \\ F4 & \(f_{4}\left(\overline{x}\right)=\max\left\{\left\{\left\{x_{i},1\leq i\leq n \right\}\right\}\right\}\) & 30 & \([-100,100]^{n}\) & 0 & Unimodal \\ F5 & \(\beta\left(\overline{x}\right)=\sum\limits_{i=1}^{n}[100\left(x_{i+1}-x_{i}^{2} \right)^{2}+\left(x_{i}-1\right)^{2}]\) & 30 & \([-30,30]^{n}\) & 0 & Unimodal \\ F6 & \(f_{6}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}\left(x_{i}+0.5\right)^{2}\) & 30 & \([-100,100]^{n}\) & 0 & Unimodal \\ F7 & \(f_{7}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}x_{i}^{4}+\text{random}[0,1]\) & 30 & \([-1.28,1.28]^{n}\) & 0 & Unimodal \\ F8 & \(f_{8}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}\sum\limits_{i=1}^{n}\left( \sqrt{x_{i}}\right)\) & 30 & \([-500,500]^{n}\) & \(-12,569.5\) & Multimodal \\ F9 & \(f_{9}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}x_{i}^{2}-10\cos(2\pi x_{i })+10\) & 30 & \([-5.12,5.12]^{n}\) & 0 & Multimodal \\ F10 & \(f_{10}\left(\overline{x}\right)=-20\exp\left(-0.2\left\{\sum\limits_{i=1}^{n} \frac{x_{i}^{2}}{x_{i-1}^{2}}x_{i}^{2}-\exp\left(\frac{1}{\overline{x}}\cos(2 \pi x_{i})\right)+20+e\right.\right.\) & 30 & \([-32,32]^{n}\) & 0 & Multimodal \\ F11 & \(f_{11}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}\frac{x_{i}^{2}}{x_{i-1}^{ 2}}\sin\left(\frac{x_{i}^{2}}{x_{i}^{2}}\right)-1\) & 30 & \([-600,600]^{n}\) & 0 & Multimodal \\ F12 & \(f_{12}\left(\overline{x}\right)=\frac{n}{10}\left\{10\sin^{2}(\pi y_{1})+ \sum\limits_{i=1}^{n}(y_{i}-1)\left[1+10\sin^{2}(\pi y_{i+1})\right]\right\}\) & & & \\ F12 & \(+(y_{n}-1)^{2}\) & 30 & \([-50,50]^{n}\) & 0 & Multimodal \\  & \(+\sum\limits_{i=1}^{n}\mu(x_{i},10,100,4),y_{i}=1+\frac{x_{i}41}{2},\mu(x_{i}, \mu(x_{i},k,m)=\left\{\begin{array}{c}k(x_{i}-a)^{m}x_{i}>a\\ 0-a<x_{i}<a\\ -k(x_{i}-a)x_{i}<a-a\end{array}\right.\) & & & \\ F13 & \(f_{3}\left(\overline{x}\right)=0.1\left\{\begin{array}{c}\sin^{2}(3\pi x_{i })+\sum\limits_{i=1}^{n}(x_{i}-1)^{2}\left[1+\sin^{2}(3\pi x_{i}+1)\right]\\ +(x_{i}-1)^{2}\left[1+\sin^{2}(2\pi x_{i})\right]\end{array}\right\}\) & 30 & \([-50,50]^{n}\) & 0 & Multimodal \\  & \(+\sum\limits_{i=1}^{n}\mu(x_{i},5,100,4)\) & & & & \\ F14 & \(f_{14}\left(\overline{x}\right)=\left(\frac{1}{500}+\frac{25}{\sum\limits_{i=1}^{n} \frac{1}{\sum\limits_{i=1}^{n}(x_{i}-a)^{2}}}\right)^{-1}\) & 2 & \([-65,65]^{n}\) & 1 & Fixed-dimension \\ F15 & \(f_{15}\left(\overline{x}\right)=\sum\limits_{i=1}^{n}\left[a_{i}-\frac{x_{i}( 2)+x_{i}}{P_{i}^{2}+b>a_{i}}\right]^{2}\) & 4 & \([-5,5]^{n}\) & 0.00030 & Fixed-dimension \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benchmark functions.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 2 Methodology
### Databases and Paper Selection  
Our main search tool was the DTU FindIt [6] tool/library, which offers access to a wide variety of papers and articles provided to DTU students and staff. As students of the university, we had access to a larger variety of resources compared to a user without login credentials; therefore, a logged-in account is necessary to replicate the results described in this section. This tool aggregates search results from different research databases (although sometimes partially [7]), including the Institute of Electrical and Electronics Engineers (IEEE), Association for Computing Machinery (ACM), and arXiv, and provides access to full-text papers.  
Additionally, we also used IEEE Xplore and its search tool. This library is a leading digital collection for scientific and technical research, providing access to peer-reviewed journals, conference proceedings, and technical standards published by the IEEE and its partners. It is widely recognized as a critical resource for engineering, computer science, and technology research. Compared to our search with DTU FindIt, we were able to find new studies with IEEE Xplore and append our list of publications considered.  
We also inspected the Association for Computing Machinery (ACM) library, a widely respected resource offering access to peer-reviewed journal articles, and arXiv, an open access repository for pre-prints and research articles independent of DTU FindIt; however, the results searching these databases were already covered by DTU FindIt's search results in full.  
As these databases' search tools allow for an advanced query, we opted for a detailed search term with multiple keywords to identify the specific papers to be included in this review. Our query process went as follows:  
1. Locate DTU FindIt and IEEE Xplore database (DTU login necessary);
2. Search for all metadata containing (\"IoT\" OR \"Internet of Things\") AND (\"Intrusion Detection System\" or \"IDS\");
3. Search for abstract criteria, containing (\"ML\" OR \"Machine Learning\") AND (\"IoT network\" OR \"IoT networks\") AND (\"identification\" OR \"detection\") AND (\"DDOS\" OR \"Distributed Denial of Service\");
4. Take items publicized only between 2019 and 30 November 2024. Our searches resulted in the following number of hits per database:DTU FindIt: 65
* IEEE Xplore: 20  
These contained 15 duplicates in total; therefore, we concluded our search by identifying 70 unique papers.  
During the selection process, certain papers identified in the initial database searches were excluded for a variety of reasons. Some studies were removed due to incomplete documentation, lack of peer review, or insufficient methodological details, which limited their reliability for synthesis. Others were excluded because their scope extended beyond the focus of this review. Duplicate records and papers presenting redundant findings were also filtered out to ensure a focused and high-quality set of papers for further analysis.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 3 Challenges of RRM in Cognitive HTS Systems  
In the cognitive HTS system with diverse interference, RRM plays a decisive role in system throughput promotion. From a holistic viewpoint, we summarize the challenges for the implementation of RRM in cognitive HTS systems as follows.
## 3 Proposed System
### Enhanced RTT & RTO Calculations in TFRC
#### 3.2.2 ALI with Mean  
Let \(\{S_{i}\}_{i=1,\;2,\;3\dots}\) be the sequence of loss intervals observed by the receiver. Generate a sequence \(\{\widetilde{S}_{i}\}_{i=1,\;2,\;3\dots}\) as the most recent \(k\) sequences of loss intervals derived from set \(\{S_{i}\}_{i=1,\;2,\;3\dots}\) and are updated on the basis of most recent sample. Let us denote \(S_{\mathrm{min}}\) and \(S_{\mathrm{max}}\) are the minimum and maximum values of the sequence \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\) Find out the mean value of all loss intervals from the sequence \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\) and is denoted by \(S_{\mathrm{mean}}\). Replace the minimum \(S_{\mathrm{min}}\) and maximum \(S_{\mathrm{max}}\) values from the sequence \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\) with \(S_{\mathrm{mean}}\) individually. Now the sequence \(\{\widetilde{S}_{i}\}_{i=1,\;2,\;3\dots}\) contains \(k\) most recent samples with two new values. These result to estimate average loss intervals more accurately with fewer samples. The corresponding weights for \(S_{\mathrm{mean}}\) are weights of minimum \(S_{\mathrm{min}}\) and maximum \(S_{\mathrm{max}}\) of k most recent sequences of \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\).  
Using the basic ALI method, the weighted average of the last n intervals \(\widetilde{S}_{(1,n)}\) and loss event rate can be calculated as \(1/\widetilde{S}_{(1,n)}\). Mathematically represented as  
\[\text{S}_{\text{min}}=\text{minimum}\ \{S_{i}^{\sim}\}_{i=1,\ 2,\ 3\ \dots}\ \ \text{S}_{\text{max}}=\text{maximum}\ \{S_{i}^{\sim}\}_{i=1,\ 2,\ 3\ \dots}\]  
\[\text{S}_{\text{mean}}=\frac{\sum_{i=1}^{k}S_{i}}{k}\text{S}_{i}\ \epsilon\ \{S_{i}^{\sim}\}_{i=1,\ 2,\ 3}\ \widetilde{S}_{(1,n)}=\frac{\sum_{i=0}^{k-1}W_{i} \widetilde{S}_{n-i}}{\sum_{i=0}^{k-1}W_{i}} \tag{9}\]  
Alternatively, we can include \(\text{S}_{\text{mean}}\) into the sequence \(\{S_{i}^{\sim}\}_{i=1,\ 2,\ 3\ \dots}\) along with \(\text{S}_{\text{min}}\) and \(\text{S}_{\text{max}}\), then weighted average of the last \(n\) intervals \(\widetilde{S}_{(1,n)}\) can be calculated as  
\[\widetilde{S}_{(1,n)}=\frac{\sum_{i=0}^{k}W_{i}\widetilde{S}_{n-i}}{\sum_{i=0 }^{k}W_{i}} \tag{10}\]
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Sponge-Like Graphene ECG Electrodes  
Porous, soft, and fluffy spongy graphene materials have unique advantages as skin-contact electrodes because of their large surface area, light weight, and flexibility. In this section, we will introduce some typical graphene ECG electrodes with sponge-like structures and show their advantages in ECG monitoring.
## 4 Routing Protocols Using Ant-Like Mobile Agents
### GPS/Ant-Like Routing in Ad Hoc Networks (GPSAL)  
This routing algorithm, GPS/Ant-Like Routing in _Ad Hoc_ Networks (GPSAL), assumes all the mobile nodes participating in the network possess GPS (Global Positioning System) capability [45]. In the routing table of each node in the network, the participating nodes are listed with the current positions and time stamps. The table also shows whether a certain node is mobile or not. When a new corner wishes to take part in the network, it listens to the wireless data to find a member. On discovery, it sends its own routing table to the node and asks to be registered. The route from the source node to the destination is found by a shortest path algorithm based on the physical locations. The routing information in the intermediate node is copied to the data packet with time stamps. If a node does not have the route to its destination, it asks the closest non-mobile node to forward its request for a route to the destination.  
Ant-like agents are used to update the routing tables of the network. They do this proactively. Similar to the other ant-based algorithms, they are sent to randomly selected destination nodes. When a node receives the ant agent, it compare the routing information in the ant agent with the one in the routing table, and copies the better information to the routing table as well as the history list of the ant agent. To explore the network, the ant agents select their next hop from the oldest entries in the routing table or the physically farthest nodes in the network. Once the ant agent arrives at the destination, it is made return to its origin.
## 5 Experimental Validation and Benchmarking  
This section provides experimental validation of both the MIMO designs considered earlier in this section. Measurement data are compared with the EM simulation results.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 5 Use Cases Deployment
### Slice Creation and Activation  
Next, a customized and dedicated slice is created. In particular, each slice is conceived as a collection of compute, network and radio chunks, as logical and isolated partitions over the common infrastructure. To perform this step, the media vertical tenant composes an end-to-end slice by selecting the desired compute, network and radio infrastructure resources and specifying the requirements to be allocated into the slice. The slice creation request is then processed by the Slice Manager, which interacts with other platform components (i.e., OpenStack, as VIM; OSM, as NFVO; and the RAN Controller, as radio devices manager) to create the required chunks at each network segment.  
Following the slice creation, the next step is its activation. Essentially, as the considered media vertical use case requires cellular access for the final users to consume the media application via smartphones attached to the slice, the activation step consists of the instantiation of an open-source mobile core server [56] together with the required configurations of the radio access nodes included in the slice. These configurations include setting the Public Land Mobile Network ID (PLMNID) that is assigned to that slice. Similarly, when Wi-Fi nodes are part of the wireless chunk of the slice, a DHCP server is automatically deployed by the platform as well, to support the service operation in terms of IP addresses allocation.
# Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks
## 3 CTI-Aware Rate Adaptation Framework
### CTI Identification
To perform conservative interference mitigation, when multiple signals of the same type are present within a sample, we set the signal with the highest average power level as the representative average power for that signal type. In constructing the signal identification model, computational complexity and execution speed must be carefully considered. Since the model runs on systems with limited computing resources, such as AP, simply choosing a model that provides the highest accuracy may not be desirable. An appropriate balance should be found by considering the impact of input sample size, the model parameters, and the size of the output layer (number of CTI signal types to identify) on the model's processing speed. The computational complexity of the proposed model can be expressed as follows:  
\[TC= \underbrace{O\left(W\times H\times C\times C_{1}\times K_{1}^{2} \right)}_{\text{Input Complexity}}\] \[+\underbrace{O\left(\sum_{l=2}^{L}\left(K_{l}^{2}\times C_{l-1} \times C_{l}\times\frac{W_{l}\times H_{l}}{S_{l}^{2}}\right)\right)}_{\text{ Model Complexity}} \tag{3}\] \[+\underbrace{O(F\times N)}_{\text{Output Complexity}},\]  
where:  
\(W\), \(H\), \(C\): input sample size (width, height, and channels);  
\(C_{1}\), \(K_{1}\): number of output channels and kernel size in the first layer;  
\(L\): total number of layers;  
\(K_{l}\): kernel size of layer \(l\);  
\(C_{l-1}\), \(C_{l}\): number of output channels from the previous layer and current layer, respectively;  
\(W_{l}\), \(H_{l}\): width and height of the output feature map at layer \(l\);  
\(S_{l}\): stride at layer \(l\);  
\(F\): number of parameters in the final fully connected layer;  
\(N\): number of final output labels. Based on these equations, appropriate parameters need to be selected considering the presence of devices causing interference in the target network and the computing resources of the system where the model runs. This allows balancing the model's accuracy and execution speed to support real-time signal identification in the target operating environment. For example, in MobileNetV3, the width multiplier (\(\alpha\), \(0<\alpha\leq 1\)) can be used to significantly reduce computational load under certain performance constraints. By applying \(\alpha\), layers can be made narrower, and the computational cost of the layers with \(\alpha\) applied is reduced to  
\[\alpha^{2}\times K_{l}^{2}\times C_{l-1}\times C_{l}\times\frac{W_{l}\times H _{l}}{S_{l}^{2}}. \tag{4}\]  
Additionally, to reduce the number of output labels when designing the model, similar signals within CTI can be grouped into the same category [26].
## 1 Introduction
These mentioned methods usually need to know the approximate frequency value of a single measured signal and then use some kind of frequency compensation strategies to diminish the frequency error between the reference signal and the measured signal. In the actual application, the low SNR signal to be measured usually contains multiple frequency components [26], which need to compensate for the frequency errors of the reference signals and estimate the amplitude of each frequency component. The traditional DLIA needs a number of individual modules in parallel to realize the measurement of multiple frequency components, which increases the consumption of space and resources. For the parameter estimation issue of the low SNR signal with multiple frequency components, this paper proposed an iterative-based software implementation of the digital lock-in amplifier with automatic frequency estimation ability. For the low SNR measured signal with a known number of frequency components, this proposed method can automatically estimate the frequency value of each frequency component. Then, this frequency value is used to generate corresponding reference signals for the DLIA to estimate the amplitude information. Moreover, this software implementation structure increases the adaptability of the DLIA, and it can be easily modified to other applications as a pre-processing step to extract signal parameters for post-signal classification or other tasks. In this article, the principles of the lock-in amplifier and frequency estimation method are discussed in Section 2. In Section 3, the necessary details that are used to implement the proposed method are stated. Then, we examine the frequencies and amplitudes estimation accuracy and the noise suppression ability of the proposed method in Section 4. Finally, we discuss the benefit of the proposed method and give a conclusion in Section 5.
# Design of a Compact 1-MV-Class Radial Closing Switch for Tesla Type Generator
## 2 General Consideration
### General Considerations Related to the Switch Design  
Figure 2 shows the generic design of the radial switch, in which the outer diameter of the main switch insulators is determined by the outer diameter of the Tesla transformer itself (490 mm), while the axial length can be adjusted as required. As the main switch needs to operate under a voltage approaching 1 MV, a high-pressure gas is chosen as a convenient insulation medium, which is finally fixed as SF\({}_{6}\), as it requires about three times less pressure than N\({}_{2}\) to achieve the same electric field breakdown stress [28]. The following characteristics are required to be observed during the switch design:  
1. The SF\({}_{6}\) gas should breakdown electrically at a pre-determined voltage, i.e., no \"mist fires\" are allowed;
2. The SF\({}_{6}\) gas should be capable to maintaining insulation integrity up to a pre-determined voltage, i.e., no \"pre-fires\" are allowed;  
Figure 1: 3-D model (**a**) and equivalent circuit (**b**) of the Tesla transformer-driven system for PEF food processing. 1, 2—mechanical supports for primary and secondary windings; Cp, Cs—the primary and the secondary capacitors of the Tesla transformer (TT); Lp, Ls—the primary and the secondary inductors of TT; Sp, Ss—the primary and the (secondary) main switch of TT; LG—the grounding inductor of TT.  
3. The Front and Back Insulators should not allow electric flashover phenomena over their surfaces facing the gas chamber;
4. The oil-insulator surface of the Front Insulator should not allow surface flashover phenomena;
5. The water-insulator surface of the Back Insulator should not allow surface flashover phenomena;
6. The volume of the switch gas chamber should be made as small as possible.
## 3 MRAM TAPUF Design  
In this design, we exploit the electrical resistances of two MRAM cells obtained from different MRAM devices using a technique called differential cell pairing. This approach allows us to combine and match various MRAM devices, creating a new set of CRPs. Therefore, the number of PUF configurations is \(\binom{p}{2}\), where \(p\) is the population of MRAM devices.  
The number of CRPs in this design is determined by the square of the MRAM cells on each device, denoted as \(n\). For example, if there are 16 MRAM cells on each device, the number of CRPs would be \(16^{2}=256\).  
MRAM cells exhibit distinct physical variations among themselves, even when they are programmed to the same state. These variations are reflected in the electrical resistance of the MRAM cells, which is influenced by small physical discrepancies that occur during the manufacturing process.  
The MRAM TAPUF follows a specific operational pattern. It receives a challenge from a server accompanied by enrollment information. The challenge comprises a pair of addresses indicating the locations of two MRAM cells. The electrical resistances of these MRAM cells are then converted into two voltages, and a response is generated based on a simple comparison of the voltages. A diagram of this TAPUF design can be found in Figure 2.  
The electrical resistances from a pair of MRAM cells are extracted and used to generate a ternary response (e.g., 0, X, 1). In this process, the locations of the MRAM cells in the pair are the challenge, and the comparison of the cells' resistance is the response. Additionally, the resistance from each MRAM cell is extracted and then converted to voltage for enrollment and comparison. This is discussed further in Section 4.  
This design has two main steps: enrollment and bit generation.