esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
## 3 Results
### Evaluations on CICIDS2017 Dataset
#### 3.2.1 Data Preprocessing Results  
We split the Thursday data file into four parts for snort to read and detect in a continuous way. More than 64,000 alerts generated by snort are recorded in a file. About 5876 intrusion actions are extracted and 39 classes of intrusion actions remain after clustering. After that, 780 intrusion sessions are extracted and only 28 sessions have more than two actions. After data preprocessing, 28 distinct intrusion sessions consisting of 39 classes of actions are extracted. The statistical results are listed in Table 2.  
In Table 2, about 2040 raw alerts (3.2% of total alerts) are used to construct the actions and sessions, the rest of alerts are merged or discarded by system. The system reduces the number of alerts by two ways: (1) intrusion action extraction, the similar alerts (with small edit distance of the alert type) will be merged to express the same action; (2) action clustering, 5876 actions are clustered into 39 classes, which can significantly reduce the number of alerts; and (3) intrusion session reconstruction, which can reduce the actions in a session. For the statistical requirements of the experiments, each intermediate process' results are saved in the files. The data related to the real intrusion can be very sparse compared to the whole dataset. The extracted intrusion actions are listed in Table 3, and some actions consist of multiple alerts.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Names** & **Values** \\ \hline Total raw alerts & 64,000 \\ Total alerts used to construct the sessions & 2040 (3.2\% of total alerts) \\ Alert groups & 769 \\ Extracted actions (repeated) & 5876 \\ Action classes (after clustering) & 39 \\ Extracted sessions & 780 \\ Sessions with only one action & 702 (90\% of total sessions) \\ Sessions with more than two actions & 28 (3.5\% of total sessions) \\ Session refining rate 1 & 82\% \\ Sessions refined & 38 (49\% of sessions with more than one actions) \\ \hline \hline \end{tabular} \({}^{1}\)\(refining=\frac{\#total\ actions\ removed\ from\ refined\ sessions}{\#total\ actions\ of\ refined\ sessions}\times 100\%\).  
\end{table}
Table 2: Statistical results of the data preprocessing.  
Compared with the previous work [24], there are two improvements in the data processing: (1) The total number of the extracted intrusion actions decreased significantly due to the action clustering which can reduce the similar actions in a session and make the semantic information more clearly, while, at the same time, it saves many computing resources; and (2) the rebuilt sessions are more accurate in expressing the intrusion strategies. This is because the complicated binary relations are simplified. Most importantly, it highlights the semantics hiding in the sessions.
# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 2 Related Work
### Summary and Implications  
The literature highlighted the impact of various 3D modeling technologies, including LiDAR and photogrammetry criminal investigation procedures, crime scene documentation, and evidence integrity preservation. Studies have also shown that the built-in LiDAR capabilities of handheld devices like the iPhone Pro and iPad Pro can swiftly reconstruct crime scenes using point clouds produced from the LiDAR sensors and the iPhone camera with the help of various applications, devices, and cloud processing. For instance, [PERSON] et al. [19] demonstrated how an iPad could complete a whole scan in 15 min, while the same scene could take hours to be completed when done by a professional-grade scanner, with accuracy suitable for legal procedures.  
Moreover, photogrammetry is increasingly employed for crime scene reconstruction, playing a crucial role in courtrooms to illustrate complex crime scenes effortlessly, as shown in the study by [PERSON] et al. [20]. Their analysis revealed that using the images obtained from the iPad in conjunction with photogrammetry software to create 3D models can be used to support evidence. Likewise, the current research examines how the built-in LiDAR technologies in mobile phones perform when compared to traditional techniques, as shown by [PERSON] et al. [19]. The study also demonstrated the practicality of portable handheld devices without proper training. Moreover, the usage of 3D technologies was explored in various forensic science fields, such as vehicle collision accidents, as in [PERSON] et al. [21], analyzing the interaction of vehicles, which is usually challenging when using traditional methods.  
Nevertheless, despite the promising results shown in the literature, various gaps need to be addressed:  
* There is a lack of studies directly comparing the efficiency and practicality of LiDAR versus photogrammetry to establish a ground for forensic science application.
* There is insufficient field testing to implement these technologies in various environmental settings.
* There is limited research on how well these technologies perform if individuals lack training in these technologies, like first responders.
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 3 Materials and Methods
### Methodology
#### 3.5.3 Point Anomalies--SESD (Seasonal ESD)  
Another type of anomaly we seek to identify are point anomalies, i.e., points that show a significant deviation from the rest of the data.  
For this, we employ the Seasonal Extreme Studentized Deviate (SESD) algorithm [26], which involves applying the ESD (Extreme Studentized Deviate) algorithm [27] to the result obtained after performing the STL decomposition of the series, which, in our case, is based on the previously calculated decomposition. The ESD (Extreme Studentized Deviate) algorithm is a statistical test used to detect multiple outliers in a univariate dataset. Unlike other tests, such as the Grubbs' test [28], which can only detect a single outlier at a time, the ESD can identify multiple outliers simultaneously.  
When selecting point anomalies, we discard those that have been identified as collective anomalies in the previous section. Point anomalies are chosen in descending order of residual value, with the most relevant being those with a high residual value.
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 4 Implementation of the Wireless Node
### Hardware Node: UCODALI Board  
This section will include a description of the hardware of UCODALI board, version 2, shown in Figure 9.  
Until now, two versions of the UCODALI board have been designed to implement the sensor network node with the DALI interface. Both versions are very similar. Its block diagram is shown in Figure 10, in which the following can be distinguished: the power supply, CC2530 OEM module [38], DALI interface, selector of the nodeID and HMI (buttons and LEDs) blocks.
## 3 Results and Discussion
### Proposed Reaction Pathway for DAP Mineralization  
The reaction pathway of Figure 10 is proposed for the mineralization of DAP under optimal conditions. The reaction is initiated by the attack of hydroxyl radicals \({}^{\bullet}\)OH on the carbonyl group of the benzene ring of DAP (1) leading to the formation of phthalic acid (3) and allyl alcohol group (4), which is spontaneously transformed into carboxylic acid. Phthalic acid could be easily transformed into phthalic acid derivatives (5) and (6) by hydroxylation. These compounds are very unstable and hence its absence in the detected products of Fenton oxidation of DAP. Moreover, subsequent oxidation with decarboxylation of phthalic acid derivatives leads to the formation of 1,2-dihydroxybenzene (7) for (6) and 1,2,4-trihydroxybenzene (8) for (5). 1,2-dihydroxybenzene is quickly oxidized to 1,2,4-trihydroxybenzene by the attack of the hydroxyl radical. 1,2,4-trihydroxybenzene then undergoes rapid ring opening to produce muonic acid (9), which is rapidly transformed into maleic acid (10), oxalic acid (11) and formic acid (12). Oxalic and formic acids end up being slowly mineralized into CO\({}_{2}\) and H\({}_{2}\)O. Oxalic acid (11) accumulated to a much larger extent than the other acids, being the most persistent of all of them.  
Figure 9: Evolution of the concentration of aromatic and aliphatic intermediates detected during the Fenton degradation of aqueous solution containing 100 ppm DAP. Experimental conditions: [DAP]\({}_{0}\) = 100 mg/L; [H\({}_{2}\)O\({}_{2}\)] = 1000 mg/L; [Fe\({}^{2+}\)] = 50 mg/L; pH = 3.2.  
The Fenton process generates hydroxyl radical, which accounts for substrate removal. However, some other radicals, like the superoxide radical \(O_{2}^{\bullet-}\) can also be produced. The addition of ethanol (as HO\({}^{\bullet}\) radicals scavenger) after 30 min from the starting of the reaction showed that DAP concentration remained constant (data not showed here). This confirms that DAP degradation occurs via direct oxidation with hydroxyl radicals rather than other radicals, which is in agreement with other studies reported in the literature [34,35].
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 4 Experiments
### Comparisons with Previous Methods  
In order to verify the effectiveness of the methods in this paper, experiments were conducted on two benchmark datasets and compared with some previous methods. One benchmark dataset was the multi-oriented text detection dataset ICDAR2015, and the other was the multi-language text detection benchmark dataset MSRA-TD500.  
As can be seen in Table 1, the experimental results of the proposed method in this paper were higher than those of DBNet and other methods on the multi-oriented text detection dataset ICDAR2015, and the F1-score value was improved by 0.5% compared to DBNet.  
Figure 5: The training and testing curves of the ICDAR2015 dataset. (**a**) The loss curves in training; (**b**) the F1-score curves in testing.  
Figure 6: The training and testing curves of the MSRA-TD500 dataset. (**a**) The loss curves in training; (**b**) the F1-score curves in testing.  
The experimental results on the MSRA-TD500 dataset also showed the robustness of the proposed method on the multilingual text detection dataset in this paper. As can be seen in Table 2, the proposed method in this paper improved by 3.5% over DBNet in F1-score, while it improved by 0.3% in Precision and 6.1% in Recall.
## 2 Methodology
### Statistical Analysis
#### 2.4.1 Calculation of the Energy under Gravity  
The first step of the statistical analysis is to calculate the energy of all rock blocks of the jointed rock slope under the action of gravity from the initial time \(T_{0}\) to \(T_{1}\), \(T_{2}\),..., \(T_{n-1}\), \(T_{n}\).  
Based on the positions and volumes of the rock blocks obtained in Section 2.3, the energy of all rock blocks under the action of gravity could be calculated from the initial state to any time thereafter. For a three-dimensional jointed rock slope, when the slope is under the action of gravity for an extended time, some rock blocks on the slope would slide. The change in the position of a rock block on the rock slope from one moment to another is shown in Figure 3. The total displacement of the rock block from \(T_{0}\) to \(T_{n}\) is \(\Delta d\). The total displacement \(\Delta d\) could be decomposed into \(\Delta x\) in the X-direction, \(\Delta y\) in the Y-direction, and \(\Delta z\) in the Z-direction, so \(\Delta d\) could be expressed as Equation (1).  
\[\Delta d=\sqrt{\Delta x^{2}+\Delta y^{2}+\Delta z^{2}} \tag{1}\]  
\(\Delta d\)--The total displacement (m);  
\(\Delta x\)--Displacement in the X-direction (m);  
\(\Delta y\)--Displacement in the Y-direction (m);  
\(\Delta z\)--Displacement in the Z-direction (m).  
The energy could be expressed as the force multiplied by the displacement in the direction of the force. Therefore, the energy under gravity is equal to gravity multiplied by \(\Delta z\). \(\Delta z\) is equal to the z coordinate of the center position of the rock block at time \(T_{n}\) minus the z coordinate of the center position of the rock block at time \(T_{0}\). The coordinates of the center positions of all rock blocks are obtained in Section 2.3. In addition, the density is a physical and mechanical parameter determined before the iterative calculation of the DEM. Therefore, the energy of all rock blocks under the action of gravity could be calculated according to Equation (2).  
\[Energy=\rho_{n}v_{n}g\Delta z_{n} \tag{2}\]  
Energy--Energy under gravity (N-m);  
\(\rho_{n}\)--Density (kg/m\({}^{3}\));  
\(v_{n}\)--Volume (m\({}^{3}\));  
\(g\)--Acceleration of gravity (N/kg);  
\(\Delta z_{n}\)--Displacement in the Z-direction (m).  
The proposed method based on energy correlation coefficient in this paper is an extension of the displacement-based method; this is because the proposed method considers the mass or weight and displacement of each block. The proposed method is suitable for slopes without obvious potential sliding surfaces. The displacement-based method considers the mass or weight of each block to be one, but the weight of each block obtained by the proposed method is the actual value calculated by the volume and density. Meanwhile, the proposed method considers both the temporal correlation and spatial correlation. Therefore, the proposed method based on energy correlation coefficient is more accurate, more comprehensive, and has wider applicability.
# Setonix: Blockchain-Based Hierarchy Domain Name System for Web3
## 1 Introduction
### Basic Concepts and Services Relevant to This Paper
#### 1.1.4 Handshake DNS  
Handshake aims to replace the centralized root zone file and root servers, making the root zone censorship-resistant and usable without permission. Additionally, it differs in purpose from ENS, as it seeks to eliminate centralized gatekeepers by addressing criticisms of ICANN [22], which has been viewed as unfair, illegal, anti-competitive, and unrepresentative of diverse internet users [23] since the early days of internet governance. By managing the root zone file, Handshake takes the position of decentralized management of TLDs. Therefore, it includes existing TLDs while also serving its own TLDs. For existing TLDs, Handshake pre-reserves domains for the top 100,000 domains on Alexa, including stakeholders who already own these domains, aiming to replace the existing DNS as much as possible.  
The blockchain network that replaces the root zone is based on the Proof of Work (PoW) consensus algorithm and uses the Hashcash function to prevent Sybil attacks and achieve consensus. Domain registration follows an auction process involving bidding, revealing, selecting the highest bidder, and then registering the domain. The payment is determined using the Vickrey auction method, where the winner pays the second-highest bid. By using the Vickrey auction method, participants are encouraged to submit bids that reflect their true value, which helps prevent price sniping and assists in discovering the true market value of domain names. However, due to the adoption of PoW, which has a structurally low TPS, and its complex processes are similar to ENS, its performance is significantly poorer compared to conventional DNS systems.  
In domain name resolving, running a full node to resolve names is burdensome for users, so Simplified Payment Verification (SPV) allows lightweight clients to participate in the Handshake network and resolve names. The SPV consists of a p2p layer, authorized name server, recursive server, and non-recursive resolver. The p2p layer synchronizes block headers and verifies name tree proofs, the authorized name server converts on-chain resources into DNS responses, and the recursive server sets its root hints and trust anchorsto its own authoritative root server, rather than ICANNs. Lastly, the non-recursive resolver resides in the authoritative layer and acts as a fallback to ICANN's system. This is used when a reserved top-level domain has not yet been claimed.
# Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System
## 4 Summary  
This paper provides an overview of the IEEE802.16e LDPC code, including the construction of its \(H\)-matrix, the fast iterative coding algorithm, and the BP decoding algorithm. Additionally, it describes the PM-DQPSK coherent optical transmission system and explores the application of LDPC within this system. By comparing the performance of the system before and after the integration of LDPC encoding, the benefits and effectiveness of LDPC in the optical communication domain are verified. In subsequent steps, LDPC with modified code rate or other compilation algorithms can be applied in coherent optical transmission systems for further evaluation and comparison. These variations will help to study the effects of different LDPC parameters or configurations on system performance, thus deepening the understanding of the adaptability and utility of LDPC in optical communication systems. The incorporation of LDPC technology into coherent optical transmission systems significantly enhances the performance and reliability of modern optical communication systems across diverse applications and environments.  
Writing--original draft preparation, J.X.; writing--review and editing, Y.L.; conceptualization, J.X. and Y.Z. ([PERSON]); methodology, J.X. and X.W.; software, J.X. and Y.Z.  
Figure 5: Coding gain and BER curve of PM-DQPSK system without and with different codelengths LDPC.  
([PERSON]); visualization, Y.Z. ([PERSON]); supervision, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61901301.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Transmitter and Receiver Circuits for a High-Speed Polymer Fiber-Based PAM-4 Communication Link
## 2 Design of the RF-DAC-Based PAM-4 Modulator
### Measurement Results of the PAM-4 Modulator  
The PAM-4 modulator was measured in frequency domain, on-wafer, using a MPI TS200 probe station. The output S21 measurement was captured with an Anritsu VectorStar ME7838A vector network analyzer (VNA). A frequency sweep from \(70\) GHz to \(130\) GHz and \(130\) GHz to \(170\) GHz can be seen in Figure 5. The reason for the two separate measurements is that different VDI VNA extenders were used. The four different traces represent the four different input data states of the PAM-4 modulator. The highest level corresponds to data input '\(00\)', while the lowest level corresponds to data input '\(11\)'.  
The \(-3\) dB bandwidth is between \(87\) GHz and \(130\) GHz, resulting in a \(43\) GHz wideband \(-3\) dB bandwidth. The RF-DAC is still usable outside of this band, but it provides less output power. The difference in power between the highest power level '\(00\)' and the lowest power level '\(11\)' is approximately \(10\) dB for frequencies between \(90\) and \(145\) GHz.  
Input and output matching of the modulator can be seen in Figure 6.  
Figure 4: Simulated output power for different data input voltages of the MSB (D0), with different input power (LO). The LSB (D1) is set to \(0\) V, and the LO frequency is \(130\) GHz.  
Figure 5: S21 for the different digital data input. LO is port \(1\) and RF is port \(2\). The measured results are shown in the solid lines and the simulated results in the dashed lines. The LO input power is \(-6\) dBm.  
A Keysight Advanced Design System (ADS) was used to simulate and estimate the RF output power for different LO input power levels. This relationship is plotted in Figure 7. The four different traces correspond to the four different PAM-4 data input power levels. The RF output power saturates around 5 dBm output power, according to this simulation. An LO input frequency of 130 GHz was used.  
The DC power consumption of the RF-DAC was measured to be 112 mW.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
###### Abstract  
Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, pooling layers, and fully connected (FC) layer(s). In a chain-based deep neural network, the FC layers contain most of the parameters of the network, which affects memory occupancy and computational complexity. For many real-world problems, speeding up inference time is an important matter because of the hardware design implications. To deal with this problem, we propose the replacement of the FC layers with a Hopfield neural network (HNN). The proposed architecture combines both a CNN and an HNN: A pretrained CNN model is used for feature extraction, followed by an HNN, which is considered as an associative memory that saves all features created by the CNN. Then, to deal with the limitation of the storage capacity of the HNN, the proposed work uses multiple HNNs. To optimize this step, the knapsack problem formulation is proposed, and a genetic algorithm (GA) is used solve it. According to the results obtained on the Noisy MNIST Dataset, our work outperformed the state-of-the-art algorithms.  
c +
Footnote †: Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
**Keywords:** convolutional neural networks (CNN); recurrent neural network (RNN); Hopfield neural network; genetic algorithm; knapsack problem; classification
# NDN Content Store and Caching Policies: Performance Evaluation
## 3 In-Network Caching
### Caching Replacement Policies  
The size of the CS is unavoidably limited. When it is necessary to cache new content and the CS is full, some content must be replaced. The caching replacement policy decides on what content to replace. When the requested content is found in CS, a _cache hit_ occurs; if not, it is a _cache miss_. The cache miss occurs when the content is never forwarded to the node or because, having already been forwarded, it is eventually replaced. Several caching policies have been proposed, the main ones being (a) _least recently used_ (LRU)--based on access time. This policy searches and replaces the content not requested any longer; (b) _most recently used_ (MRU)--also based on access time, as in the case of LRU. In this case, the most recently requested content is replaced; (c) _least frequently used_ (LFU)--based on time and frequency of access/request. This policy searches and replaces the less often requested content. Unlike LRU or MRU, which only use time to evaluate what content to replace, this policy uses two factors, age and the access frequency; (d) _first in first out_ (FIFO)--based on time, similar to LRU, but here, the order of content arrival is taken into account, that is, differently from LRU, the new requests, finding an already stored and least recently used object, do not _refresh_ the timer associated to it [8]; (e) _priority first in first out_ (_Priority-FIFO_) [47]--based on time, similar to FIFO but with three queues of relative priorities (the unsolicited queue, the stale queue, and the FIFO queue); and (f) _random_--in this policy, the content to replace is randomly selected.  
Some extensions of these main policies can be found in reviews, such as [8; 48]. Specific reviews on NDN caching can be found in studies, such as [4; 49].