esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller
## 4 Integration of an ML-Based Cybersecurity Detector and Mitigator in the TeraflowSDN Controller
### Integration of the Cybersecurity Components in the TeraFlowSDN Architecture
#### 4.2.2 Centralized Attack Detector  
The Centralized Attack Detector (CAD) component provides IP-layer attack-detection capabilities and a consolidated attack-detection mechanism based on DAD reports. The CAD consolidates information collected from multiple instances of the DAD. This allows for the monitoring of malicious network traffic while forming a view of the security status of IP traffic. The CAD stores the information it receives from multiple instances of the DAD within a certain time interval, and this can be parameterized in a buffer. From the summarized traffic statistics received from the different instances of the DAD, this component performs attack detection using an embedded ML model.  
After the configured time interval elapses, the ML model classifies each connection in the buffer as normal traffic or as part of an attack, and a confidence level decision is derived. From this inference, the CAD produces a description of the connection, including a confidence value indicating the probability that the connection is an attack or normal traffic. If a connection is detected to be part of an attack with a confidence level at or above a configurable threshold, the CAD notifies the Attack Mitigator component with the attack description, providing a full characterization of the attack properties and other relevant information to perform an attack-mitigation strategy.  
In the current implementation, the ML model used is the Random Forest model that was previously converted and stored in the ONNX (Open Neural Network Exchange) format. This format allows the embedding of a compiled model with an optimized graph that can reduce the overall size of a model, speed up the prediction inference time and reduce the use of computing resources.  
In addition, security monitoring cycles are run periodically with a configurable time interval to collect Key Performance Indicators (KPIs) that provide an overview of the security status of the network. This information is stored in a database for further analysis and security audits. In addition, these KPIs are displayed on a Grafana dashboard to provide the network administrators with a real-time view of the current state of the network cybersecurity.
# An Artificial Neural Network Autoencoder for Insider Cyber Security Threat Detection
## 5 Discussion
### Impact of Tuning Parameters on Threat Detection
#### 5.1.2 Impact of Batch Size and Number of Epochs  
Another analysis that is related to normal and anomaly data reconstruction accuracy is presented for the tuning parameters known as batch size and number of epochs. These parameters affect the training time of the ANN model used in the analyses. The batch size determines how many training samples should be trained in a single processing slot. The higher the batch size, the faster the training. However, during training with the higher batch size, the model might not learn the data characteristics properly. Therefore, choosing the optimal batch size leads to the best performance of the model.  
Training the neural network with whole data for each single cycle creates an epoch, and increasing the number of epochs will make the model overfit instead of learning the characteristics of normal and anomaly data patterns. Therefore, increasing the number of epochs does not generalize the model to the new data. Choosing a low number of epochs  
Figure 21: Reconstruction of anomaly data with MAE and SGD.  
Figure 20: Reconstruction of normal data with MAE and SGD.  
makes the model underfit, i.e., training stops before learning the full characteristics of the data. The above-presented model used 200 epochs and 120 as its batch size. The selected number of epochs and batch size have been selected empirically after several different trials and have been found optimal for the specific proposed model. It is finally worth emphasizing that changing the number of epochs and batch size did not result in a large difference in the model prediction; however, it has a significant impact on the training time and loss.
## 3 Methodology  
The knowledge acquisition of a rule-based system that takes into consideration spatial relations is not easy, and machine learning requires numerous training cases in order to obtain a reasonable accuracy. Therefore, this study proposes a hybrid method by first developing a simple rule-based system that merely takes into consideration clash attributes; then, the results of the rule-based reasoning is merged to the dataset of machine learning. This study develops the research methodology shown in Figure 1 to validate the effectiveness of this method. First, a real architectural project is selected, BIM software is used for clash detection, and then the (clash) detection report is submitted to two experts for labeling clash types. The labeling results with identical labels from the experts are selected and further adjusted to form training dataset #1; this dataset is subjected to a supervised machine learning process to obtain the \"pure machine learning results\". At the same time, the same experts are interviewed to acquire their knowledge of determining the types of clashes and incorporate this knowledge into rules. These rules only consider attributes of clashing components and do not delve into other deeper spatial relations with others. After implementing the rules, training dataset #1 is used in the same manner for rule-based reasoning to obtain the \"pure rule-based results\". Next, these results are regarded as a field of training data inserted to training dataset #1 and form training dataset #2; this dataset is again processed by the same supervised machine learning to obtain the \"hybrid results\". Finally, the accuracy of the three prediction results is evaluated and compared. In the following paragraphs, Section 3.1 first describes the data collection process; Section 3.2 describes the process of hiring experts to label the type of case clashes for the clash detection report; and Section 3.3 describes the selection and adjustment of the labeling results. The development of the rule-based system and its prediction results are introduced in Section 4, followed by Section 5 describing the prediction results of two similar machine learning processes.
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Proposed Scheme 1: ACE
#### 3.5.2 Path Establishment Phase  
Once a node knows its neighbours, a multi-hop path is established from source to sink. Path establishment phase has two main objectives; (i) identification of the next destination and (ii) identification of relays that act as cooperative nodes for data retransmission. Source node identifies its neighbours with the help of depth information of other nodes. Nodes that have depth lower than that of source node are included in the Forwarding Neighbour (FN) list and are called FNs which are potential candidates for next hop destination. Neighbours with depth greater than source node are neglected. EEDBR algorithm is followed to select the master node from FN list for the next  
Figure 5: System model. (**a**) ACE; (**b**) E-ACE.  
hop destination. In this process, the node of lesser depth and highest residual energy is selected as master node.  
Relay nodes are identified after the selection of master node and are identified among the nodes that lie in the cooperative region as shown in Figure 2. Nodes that are deployed in cooperative region are known as cooperative nodes. In ACE, only two nodes among all cooperative nodes act as relays. Selection criteria for R1 and R2 is based on highest residual energy and minimum depth. Once a node identifies the master node and cooperative nodes, it broadcasts this information to its neighbors. Neighbors on receiving this information know, who the master node is and who the cooperative nodes are. It is assumed that control messages are not lost during transmission. The process of choosing master node and retransmission nodes continues till sink is approached.
# Optimization of Bifurcated Switching by Enhanced Synthetic Antiferromagnetic Layer
## 3 Conclusions  
In summary, we successfully fabricated an MRAM test chip using a 300 mm process, and an optimized SAF was introduced to enhance the stability of the pinning layer. The _Hf_ field was enhanced significantly from \(\sim\)5700 Oe to \(\sim\)7500 Oe, while _Hex_ and \(H_{\rm C}\) showed little change. Consequently, the bifurcated-switching phenomenon in the WER curve was significantly improved. Taking advantage of a normal WER curve, the switching voltage (WER at 1 ppm level) was reduced by 18.6%, even though the write voltage at 50% probability remained constant. The same structure and the same magnetic properties of the free layer in different MTJ stacks point to the improvement in the WER being related to the enhancement of the pinning layer. The stray field along the x-axis caused by the intrinsic PMA and thermal disturbance was thought to be the main cause of abnormal ballooning. The SAF layer was experimentally verified for the first time as having an obvious effect on  
Figure 4: (**a**) The distribution of extracted Hc in patterned device level. (**b**) Comparison of Hc distribution before and after SAF optimization. (**c**) Comparison of the MTJ array write error before and after SAF optimization. (**d**) Schematic diagram of the SAF PMA influence on WER.  
the bifurcated-switching phenomenon, as distinct from the back-hopping phenomenon. This work provides a new perspective on anomalous WER to enhance understanding of the internal mechanism of spin-flipping dynamics. At the same time, it proposes another innovative approach to improving the WER and reducing write voltage in MRAM chips.  
Methodology, Y.G. and R.C.; Formal analysis, L.Z. and T.F.; Investigation, J.G. and D.Z.; Resources, F.M.; Writing--original draft, Y.S.; Supervision, W.H. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science and Technology Major Project (Grant No. 2020 AAA009003).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
All authors were employed by Zhejiang Hikstor Technology Company Ltd. They declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 1 Introduction  
Milk and dairy products are important sources of nutrients and energy in human diets. In the wide range of milk products on the market, butter is among those recommended and invariably chosen as an important element of a diet [1]. It is highly valued for its characteristic flavour and aroma. Butter is a water-in-oil (W/O) emulsion. It is probably one of the oldest milk products, produced by the concentration of milk fat following the destabilisation of oil-in-water (O/W) milk or cream emulsion [2]. The butter is obtained by processing milk. The main steps in the production of butter are cream separation (by centrifugation of milk), cream pasteurization, cooling, maturation (physical or biological), churning, washing the butter granules, working of butter and packaging [3].  
In accordance with Appendix II in Regulation (EU) No. 1308/2013, in the European Union, there exist four trade names for products containing only milk fat: butter (80-90% milk fat content), three-quarter fat butter (i.e., 3/4 from 80%, which has between 60 and 62% milk fat content), half-fat butter (39-41% milk fat content) and dairy spread X% (milk fat content less than 39% or more than 41% but less than 60% or milk fat content more than 62% but less than 80%) [4].  
The sensory and rheological properties of milk fat products are determined by the interaction of many factors. The breed of cow, feeding, season and climate conditions all influence the fatty acid composition. Different methods for the production of butter and technological parameters of processes influence the number and size of fat crystals. Milkfat contains a wide variety of triacylglycerlycerols, and each has its own melting point, which is determined by the melting point of its fatty acids [5; 6].  
In many parts of the world, apart from traditional butter (80% milk fat), reduced fat butters (spreads) with declared fat contents are produced [7]. As regular butter must have a temperature not less than 15 \({}^{\circ}\)C before it can be easily spread [8], food manufacturers have responded by producing softer forms of butter (e.g., with a higher water content) [9].  
Another trend is adding healthy food products to milk fats. [PERSON] et al. [10] mixed lycopene with butter for aesthetic and neutraceutical purposes. [PERSON] et al. [11] showed that cinnamon extract can be used to formulate an antioxidant-rich butter, and it can be placed as a natural preservative for butter preparation.  
The aim of this research was to investigate the physicochemical characteristics and sensory preferences related to the fat content in milk fat products. This study aimed to find out which dairy fats customers like the most and why. The obtained answer would be valuable information for dairy groups that want to produce the bestselling fats (e.g., with increased health value by adding natural health-promoting ingredients).
## 4 Simulation Results Analysis  
### Performance Analysis of the EA-CA Model  
The EA-CA model is evaluated in a simulated scenario. The initial topology in the EA-CA model is connected. When an EA detects the radiation source, it transmits the information to its neighboring agents and the neighborhood agents transform their states from the reconnaissance state to the jamming state. The evolution process of the EA state information in different strategies is shown in Figure 1. The color keys in the figure indicate the evolution of each agent state. In the ECM scenarios, state Y represents the state of agent with the initial state being reconnaissance state. When the radiation source is detected, the agent state evolutes to the jamming state with the increase of reconnaissance information. The performances of different evolution strategies are determined by final convergence or divergence. It can be seen that when the AS strategy is adopted, the communication topology of the EA-CA model is not always connected during the evolution process, and the system will converge to multiple local centers. The NS strategy 1 represents dividing the neighboring agent into several sectors. The NS strategy 2 is based on the NS strategy 1, by adjusting the angle of the sectors to make the individuals of each sector evenly distributed. When using the NS strategy 1, the communication area of each agent is divided into several sectors, and the communication topology is always connected during the evolution process. The EA-CA model converges to a small area quickly, then to achieve the consensus at a very slow speed. When the NS strategy 2 is adopted, the number of agents in each sector is evenly distributed, and its convergence speed is faster than the NS strategy 1. When the communication topology of the model has not reached full connectivity, the system adopts NS strategy 2, and the convergence speed at this time is closed to the NS strategy 2. When the communication topology of the model reaches full connectivity, it switches to AS strategy; the convergence speed at this time is faster than NS strategy 2.  
The influence of different parameters, such as the number of communication sectors, the communication radius of agent, the number of agents, and the different adjustment factors on the convergence speed of the system are also analyzed.  
Figure 1: The evolution process of EA–CA model state information in different strategies.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Sensitivity Analysis of Chaotic Sequences  
In order to test the sensitivity of bit scrambling to chaotic sequences, cipher images are decrypted except for the bit-scrambling stage. Figure 8 shows the results of the tests for the image of peppers. The algorithms proposed in [1,2,4] can recognize the approximate outline of the original image from the decrypted image, and the sensitivity is poor. In addition, it is difficult to get the original information from the decrypted image by the algorithm proposed in this paper and in [7,8]. The results show that stage-merging bit scrambling and adjacent-pixel bit scrambling are both sensitive to the chaotic sequences used.
# A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay
## 5 Case Study: Simulation Results and Comparative Analysis

Table 6: Performance comparison between the setting with/ without the optimization. \begin{table} \begin{tabular}{c c c c} \hline \hline
**Protection Number** & **Action Time/s** & **Protection Number** & **Action Time/s** \\ \hline
1 & 0.7 & 10 & 1.3 \\
2 & 0.6 & 11 & 0.9 \\
3 & 1.7 & 12 & 1.5 \\
4 & 1.1 & 13 & 2.2 \\
5 & 1.5 & 14 & 1.2 \\
6 & 1.6 & 15 & 0.7 \\
7 & 1.4 & 16 & 0.5 \\
8 & 0.5 & 17 & 1.0 \\
9 & 0.9 & 18 & 1.3 \\ \hline \hline \end{tabular}  \The term \"Running time\" mentioned above refers to the duration taken by the algorithm from the initial iteration until it reaches the final convergence value. Furthermore, Figure 6 illustrates the number of iterations necessary for the convergence of various heuristic algorithms. It can be concluded from the analysis of the figure and table above that the combined IFE-QGO algorithm has certain advantages over other heuristic algorithms in terms of global convergence speed, and the number of iterations is reduced by 50.88%, 60.00%, and 69.23%, respectively. In terms of running time, the IFE-QGO method is still more efficient, which is shortened by 59.25%, 79.71%, and 87.42%, respectively, compared with the other three algorithms. Regarding the final objective function value, which represents the overall operation time of the system used for overcurrent protection, both the IFE-QGO and the QGO algorithm achieve the best results, demonstrating their superior ability for a global search. The IFE-QGO algorithm, with the assistance of IFE, enhances computational efficiency and running speed. In contrast, classical heuristic algorithms, such as PSO and GA, tend to get trapped in local optimal solutions. By employing the IFE-QGO algorithm, significant time savings of 0.507 s and 1.380 s can be achieved for the power system, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Optimal Value of Objective** & **Running Time/s** \\  & **Function** & **Running Time/s** \\ \hline IFE-QGO & 9.005 & 0.238 \\ QGO & 9.005 & 0.584 \\ PSO & 9.512 & 1.173 \\ GA & 10.385 & 1.892 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Heuristic algorithm optimization results. Figure 5: Comparison of optimized convergence curves. In summary, the IFE-QGO algorithm can help us solve the relay protection setting optimization problem in a distributed grid more accurately and quickly.
## 1 Introduction
### Steps of a Typical OCR Workflow  
The character recognition in itself only represents one subtask within an OCR workflow, which usually consists of four main steps (see Figure 1), which often can be split up into further substeps. We use the term \"OCR\" as a separate main step within the OCR workflow, as other notations like \"recognition\" would be misleading since the step comprises more sub-tasks than the text recognition alone.  
1. Preprocessing: First of all, the input images have to be prepared for further processing. Generally, this includes a step that simplifies the representation of the original color image by converting it into binary, as well as a deskewing operation in order to get the pages into an upright position. Additional routines like cropping, dewarping, denoising, or despeckling may be performed.  
Figure 1: Main steps of a typical OCR workflow. From left to right: original image, preprocessing, segmentation, OCR, postcorrection.  
Segmentation: Next, one or several segmentation steps have to be conducted, mostly depending on the material at hand and the requirements of the user. After separating the text regions from non-text areas, individual text lines or even single glyphs have to be identified. Optionally, a more fine-grained classification for non-text (images, ornaments, etc.), as well as for text elements (headings, marginalia, etc.) can be performed already on the layout level. Another important sub-task is the determination of the reading order, which defines the succession of text elements (region and/or lines) on a page.
3. OCR: The recognition of the segmented lines (or glyphs) leads to a textual representation of the printed input. Depending on the material at hand and the user requirements, this can either be performed by making use of existing mixed models and/or by training book-specific models after producing the required GT.
4. Post-processing: The raw OCR output can be further improved during a post-processing step, for example by incorporating dictionaries or language models. This step can support or replace the manual final correction phase depending on the accuracy requirements of the users.  
As for the final output, plain text, that is the (post-processed) OCR output, has to be considered the minimal solution. Additionally, several formats that can incorporate a more sophisticated output also containing layout or confidence information have been proposed, for example ALTO ([[https://www.loc.gov/standards/alto](https://www.loc.gov/standards/alto)]([https://www.loc.gov/standards/alto](https://www.loc.gov/standards/alto))), h OCR [10], or PAGE [11].
## 1 Introduction
However, constitutive VOC emissions from non-stressed plants have also been shown to influence responses in stressed and/or susceptible neighbours. In the well-documented ecological concept of VOC-mediated associational resistance (AR), susceptible focal plants have been shown to adsorb defence-related constitutively emitted VOCs from their resistant heterospecific neighbours before re-emitting them against herbivores [15; 31]. Similarly, in a drought study by [PERSON] et al. [32], it was suggested that _Acer mosspessulanum_ absorbed constitutively emitted isoprene from neighbouring _Quercus pubescens_ to improve its drought tolerance. Despite the extensive evidence from previous studies which support the concept of plant-plant communication, knowledge of the molecular basis and mechanisms involved in VOC-mediated plant-plant interactions remains scant. Using metabolome, transcriptome, and physiological analyses, this study investigated how drought and recovery affected the two-way VOC exchange between stressed and non-stressed _Vitis vinifera_ L. cv. Shiraz. Based on the hypothesis that drought induces VOCs in grapevine that can elicit defence responses in non-stressed receiver vines, this study explored the role played by water stress on VOC emissions, specifically \(\alpha\)-pinene, isoprene, MeJA, MeSA, and (Z)-3-hexen-1-ol, as well as how these VOCs induce drought defence responses and stomatal closure in well-watered (WW) receiver vines. The emissions of these VOCs were quantified under well-watered and drought stress conditions. Targeted gene expression (transcript) analysis was also carried out on genes related to the biosynthesis or signalling pathways of the above-mentioned VOCs.